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ROSMASTER M3📖
00.Configuration and Operation Guide
  • 1.Quick start handle to control the car
  • 2.Log in to the car and view the code
  • 3.Enter the Docker (For RPI5)
  • 4.Camera and Radar Configuration (Must Read)
  • 5.Assembly video
  • 6.Modify Control Board Firmware Parameters
01.Chassis Control Course
  • 01.ROS control
  • 02.Handle control
  • 03.Keyboard control
  • 04.Robot state estimation
  • 05.Data remote visualization
  • 06.Line velocity calibration
  • 07.Angular velocity calibration
  • 08.Robot URDF model
  • 09.Line patrol automatic driving
  • 10.Patrol
02.Lidar Course
  • 01.Lidar introduction and use
  • 02.Lidar point cloud fusion and filtering
  • 03.Lidar obstacle avoidance
  • 04.Lidar tracking
  • 05.Lidar guard
  • 06.Gmapping-SLAM mapping
  • 07.Cartographer-SLAM mapping
  • 08.slam_toolbox mapping
  • 09.Lifelong mapping
  • 10.Navigation2 single-point navigation avoid
  • 11.Navigation2 multi-point navigation avoid
  • 12.Cartographer relocalization navigation
  • 13.slam_toolbox localization and navigation
  • 14.Synchronous mapping and navigation
  • 15.RTAB-Map mapping
  • 16.RTAB-Map navigation
  • 17.APP mapping and navigation
03.AI Model Basics
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
04.AI Model Development
  • 01.Register a model service provider account
  • 02.Configuring API-KEY
  • 03.Introduction to Dify
  • 04.Basic Dify Features
  • 05.AI Large Model Development - chatbot
  • 06.Deploy the RAG knowledge base
  • 07.RAG knowledge base + chatbot
  • 08.AI agent workflow
  • 09.Custom wake-up response
  • 10.Core module testing tools
05.AI Model - Voice Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understanding
  • 3.Multimodal visual understanding+SLAM navigation
  • 4.Intention estimation
06.AI Model - Text Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understanding
  • 3.Multimodal visual understanding+SLAM navigation
  • 4.Intention estimation
07.Road Network Planning and Navigation
  • 1.Introduction and Quick Start
  • 2.Constructing a Pose Map
  • 3.Road Network Labelling
  • 4.Inter-Road Network Planning and Navigation
  • 5.Free-Pose Road Network Navigation
  • 6.Path Replanning
08.Autonomous Driving expand(Need purchase track map)
  • 01.Environment Setup & Precautions
  • 02.YOLO Environment Configuration
  • 03.Data Collection
  • 04.Data Annotation
  • 05.YOLOv11 Model Training
  • 06.Model Format Conversion
  • 07.Vehicle Centerline Calibration
  • 08.Lane Detection
  • 09.Lane-Keeping Autonomous Driving
  • 10.Traffic Sign Recognition
  • 11.Comprehensive Autonomous Driving Applications
  • 12.Autonomous Driving Debugging Module
  • 13.Road Network Planning with Track Map
  • 14.Road Network Planning with Traffic Sign Recognition
  • 15.AI Model with Road Network Planning
  • 16.AI Model with Track Map and SLAM Navigation
  • 17.AI Model with Road Network Planning and Autonomous Driving
09.Depth Camera Course
  • 01.Dabai_DCW2 Camera Introduction
  • 02.Nuwa-HP60C Camera Introduction
  • 03.Depth Pseudo-Color Image
  • 04.Depth Camera Distance Measurement
  • 05.Wood Block Volume Measurement
  • 06.Edge Detection
  • 07.YOLOv11 Object Detection
  • 08.YOLOv11 Object Tracking
  • 09.Color Following
  • 10.Face Following
  • 11.QR Code Following
  • 12.Machine Code Following
  • 13.KCF Object Tracking
  • 14.QR Code Motion Control
  • 15.AR Vision
10.Mediapipe Visual Course
  • 01.Hand Detection
  • 02.Posture Detection
  • 03.Overall Detection
  • 04.Facial Landmark Detection
  • 05.3D Object Recognition
  • 06.Face Detection
  • 07.Gesture Recognition
  • 08.Face Special Effects
  • 09.Brush
  • 10.Finger Control
  • 11.Palm Target Positioning
  • 12.Fingertip Trajectory Recognition
  • 13.Gesture Following
  • 14.Posture Following
  • 15.Gesture Motion Control
11.Multi-vehicle Course
  • 1.Multi-vehicle Control
  • 2.Multi-vehicle Navigation
  • 3.Multi-vehicle Platoon
12.AI Large Model Basic Course
Offline large model (Text Interaction)
  • 0. AI Large Model Mirroring Explanation
  • 1. Deployment of large-scale AI models
  • 2. Installation of the large model dialogue platform
  • 3. Meta AI Llama 3.2 model
  • 4. Alibaba Cloud Qwen3 Model
  • 5. DeepSeek DeepSeek-R1 model
  • 6. Microsoft Phi-4 mini model
  • 7. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 8. Google Gemma3 Visual Multimodal Large Model
  • 9. Llava Visual Multimodal Large Model
  • 10. Multimodal Visual Understanding Applications
  • 11.MiniCPM-V Visual Multimodal Large Model
  • 12. Multimodal Text-Based Image Application
  • 13. Multimodal Video Analysis Application
  • 14. Multimodal table scanning application
  • 15. Multimodal visual positioning applications
  • 16. Multimodal Autonomous Agent Application
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual understanding and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
Online large model (Text Interaction)
  • 1. OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal visual understanding applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
13.Control Board Course
1.Development environment setup
  • 1.Introduction to the Control Board
  • 2.Set up the STM32CUBEIDE development environment
  • 3.Burning STM32 firmware using SWD
  • 4.Burning STM32 firmware using serial port
  • 5.Compile the microros driver library
  • 6.Install and start the microros agent
  • 7.Import Project
2.Control board STM32 basic routine
  • 1.Turn on the LED light
  • 2.Button functions
  • 3.Drive the buzzer
  • 4.Serial communication
  • 5.Battery voltage detection
  • 6.Driving PWM Servo
  • 7.Drive motor
  • 8.Read motor encoder data
  • 9.PID control of motor speed
  • 10.Robot kinematics analysis theory
  • 11.Read IMU data
  • 12.Read Lidar data
  • 13.Flash access data
  • 14.Driving OLED Displays
  • 15.Driving RGB light strip
  • 16.SBUS model aircraft remote control
  • 17.USB controller remote control
  • 18.CAN bus communication
3.Control board micro_ros basic routine
  • 1.Post a topic
  • 2.Subscribe to a topic
  • 3.Multi-topic subscription and publishing
  • 4.Subscribe to the buzzer topic
  • 5.Subscribe to the bus servo topic
  • 6.Subscribe to the rate control topic
  • 7.Release Speed Topic
  • 8.Publish IMU data topic
  • 9.Publish radar data topic
14.Main Control Course
Jetson Orin Nano_NX
  • 1.Jetson Orin board introduction
  • 2.Restore the factory image system
  • 3.Backup SSD system
  • 4.SSD expansion
  • 5.Network configuration
  • 6.SSH remote login
  • 7.VNC remote control
  • 8.Remote file transfer
  • 9.Jtop tool
  • 10.Exchange space expansion
Raspberry Pi
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Update and upgrade operating system
  • 6. Introduction to raspi-config tool
  • 7. config.txt file description
  • 8. Network Configuration
  • 9. remote access
  • 10. Transfer files remotely
  • 11. Set display resolution and rotation
  • 12. Set screen to sleep
  • 13. Play audio and video
  • 14. Using USB camera
  • 15. Using MIPI camera
  • 16. Get real-time temperature of Raspberry Pi
RDKX5
  • 1.Introduction to RDK X5 board
  • 2.Write system file
  • 3.Hardware interface description
  • 4.Install case and other accessories
  • 5.Network configuration
  • 6.Remote login
  • 7.Update system
  • 8.srpi-config user guide
  • 9.config. txt configuration file
  • 10.Self-startup setting
  • 11.RDK exclusive command usage
  • 12.Linux command usage
  • 13.GPIO readwrite operation
  • 14.Image classification algorithm
  • 15.Use USB camera
  • 16.Use MIPI camera
  • 17.Binocular camera depth algorithm
15.YOLOv11 Development (ORIN)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Dataset annotation
  • 10.Model Training_Conversion
  • 11.Model prediction
16.YOLOv11 Development(RDKX5)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model prediction
  • 9.Model Quantization
17.YOLOv11 Development (RPI)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Model prediction
18.Linux Operating System
  • 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
19.ROS2 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
20.Docker Course
  • 1、Docker overview and docker installation
  • 2、Common commands for docker image containers
  • 3、Docker images deeply understand and publish images
  • 4、Docker hardware interaction and data processing
  • 5、Enter the bot's docker container
  • 6、Robot development environment construction in Docker
21.Image Processing Basics Course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Image cutting
  • 8. Image translation
  • 9. Image Mirroring
  • 10. Affine Transformation
  • 11. Image Rotation
  • 12. Perspective Transformation
  • 13. Grayscale processing
  • 14. Image Binarization
  • 15. Edge Detection
  • 16. Line drawing
  • 17. Rectangle and circle drawing
  • 18. Drawing text and pictures
Download
  • Mapping_APP(iOS_Android)
  • MainBoard_AI_Pure_System
  • Robot_Image_System_File
  • Code-Firmware
  • Instruction_Manual
  • VM_File
  • Software_Tools
  • Firmware_DriverFile
  • 3D_Model_File
  • ROS2_Basic_VM
  • Other_Annex
Download ZIP

Welcome to ROSMASTER M3 repository

10.Robot kinematics analysis theory

Robot kinematics analysis theory

Robot kinematics analysis theory1. Experimental Purpose2. Hardware Connection3. Introduction of Mecanum Wheel4. Mecanum Wheel Motion Analysis and Direction Determination6. Mecanum Wheel Installation Combination and Motion Analysis7. Installation location of Mecanum wheel8. Final installation method

 

1. Experimental Purpose

Analyze the kinematics of the car based on its structural characteristics. Since real cars have structural errors and factors like resistance and friction during motion, the kinematics are quite complex. For simplicity, we will only analyze the car in an ideal state.

2. Hardware Connection

As shown in the figure below, the STM32 control board integrates four encoder motor control interfaces. This requires additional connection to the encoder motor. The motor control interface supports 520 encoder motors. Because the encoder voltage requires high voltage and high current, a battery must be plugged in for power.

The corresponding names of the four motor interfaces are: left front wheel -> M1, left rear wheel -> M2, right front wheel -> M3, right rear wheel -> M4.

image-20250612110737050

There is a detailed line sequence silk screen near the motor interface. Here we take motor M1 as an example. M1+ and M1- are the interfaces for controlling the rotation of the motor, GND and +3.3V are the power supply circuits for the encoder, and H1A and H1B are the encoder pulse detection pins.

image-20250612100250376

3. Introduction of Mecanum Wheel

During the heyday of industrial manufacturing, traditional vehicle steering proved inconvenient in certain environments. For example, in aerospace component assembly, using traditional vehicles to transport and assemble components consumes significant manpower, material resources, and time. While accuracy is limited to a fraction of a millimeter, misalignment requires recalibration. For large, sophisticated equipment, high efficiency means a significant advantage over competitors. A method that allows a vehicle to translate and rotate at any angle without rotating the vehicle itself would be a perfect solution to this problem.

It was not until 1973 that the Swedish Mecanum company developed a more practical solution that was widely adopted, which is the Mecanum wheel we are going to introduce today.

A Mecanum wheel (hereafter referred to as a Mecanum wheel) consists of a hub and rollers surrounding the hub. The rollers are small, unpowered, driven wheels. The angle between the roller axis and the hub axis is 45 degrees. There are two types of Mecanum wheels: A and B, mirror images of each other, or sometimes referred to as left-handed and right-handed wheels. These are typically marked on the hub with the symbols A and B, or L and R.

imgimg

img img

4. Mecanum Wheel Motion Analysis and Direction Determination

As mentioned earlier, there are two types of wheels: A and B. If wheel A moves forward and to the right at the same time, that is, it moves diagonally to the right front, then on the contrary, wheel A will move backward and to the left at the same time, that is, it moves diagonally to the left rear; accordingly, wheel B can move diagonally to the left front and right rear.

With the front of the car as the positive direction, the direction of the wheel moving forward is considered the motor's forward rotation, and the direction of the wheel moving backward is considered the motor's reverse rotation. (All the following lessons will be explained in this direction)

imgimg

Take wheel A for example. The roller cannot provide forward force in the direction of movement due to rolling, and the roller cannot roll in the direction of the roller axis and rubs against the ground, generating friction force in the roller axis, that is, obliquely to the right front or left rear. Therefore, the speed direction of wheel A is obliquely to the right front or left rear. The same analysis can be applied to wheel B.

imgimg

According to the physics knowledge we learned in high school, we know that speed can be decomposed orthogonally, and the direction of the vehicle's movement depends on the combined speed of the four wheels.

Then wheel A can be decomposed into velocity components axially to the right and vertically axially forward, or velocity components axially to the left and vertically axially backward.

imgimg

In this way, the velocity component of wheel B is a mirror image of wheel A.

imgimg

6. Mecanum Wheel Installation Combination and Motion Analysis

After knowing the velocity components of wheels A and B, we can arrange the four-wheeled Mecanum wheel chassis into different combinations: AAAA, BBBB, AABB...

Do all combinations allow for full range of motion?

Below we will only give one wrong example and one correct example, and you can infer the rest on your own (actually there are too many).

Analysis error example [AAAA]

imgimg

This might seem unreliable, but let's analyze it anyway because, as an example of an error, it's easy to understand what's wrong. We previously explained that the velocity components of wheel A are either front plus right, or rear plus left.

When the four wheels rotate forward at the same time, each wheel will have a leftward velocity component, which will cause the entire chassis to move to the left at the same time when moving forward; similarly, it will inevitably move to the right when moving backward, which makes it impossible to use. This thing will run around uncontrollably, which is not the omnidirectional movement we want.

imgimg

The correct wheat wheel distribution should be [ABBA]

imgimg

When all four wheels are turning forward, the A and B wheels can cancel each other's axial speed, leaving only the forward speed. In this way, the chassis moves straight forward without deviation, and the same applies to backward.

imgimg

If wheel A rotates forward and wheel B rotates backward, the forward and backward speeds will cancel each other out, leaving only the rightward speed, and the chassis will move horizontally to the right.

img

On the contrary, if wheel A rotates in reverse and wheel B rotates in forward direction, it will move horizontally to the left;

img

Finally, the left wheel of the chassis rotates forward and the right wheel rotates backward, so that the chassis can rotate to the right; otherwise, the chassis will rotate to the left.

imgimg

In addition, when wheel A rotates forward and wheel B is stationary, the chassis will move to the right front;

When wheel A reverses and wheel B remains stationary, the chassis moves to the left rear.

imgimg

Accordingly, wheel A remains stationary, wheel B rotates forward, and the chassis moves to the left front;

Wheel A remains stationary, wheel B rotates in reverse, and the chassis moves to the right rear.

imgimg

There are also four other movement modes that rotate around the midpoint of the axis.

imgimg

imgimg

[Thinking] Are the above four situations very similar to translation? When the two stationary wheels move like translation, but the speed is lower than that of the other two wheels, how will the McLennan chassis move?

7. Installation location of Mecanum wheel

We have explained the installation combination of the McReels above. There are also some considerations when installing the four McReels.

The shapes formed by the points of contact between the four wheels and the ground are mainly squares and rectangles. Although there are some unusual shapes, we will only analyze squares and rectangles here.

Square: The four wheels are located at the four vertices of a square, allowing for both translation and rotation. However, due to factors such as the shape and size of the robot chassis, this installation method, while ideal, is rarely possible.

imgimg

The chassis we use has a carefully designed square shape after adding the McDonnell Douglas wheels, making it the most ideal McDonnell Douglas wheel chassis.

Rectangular: Wheel rotation generates yaw-axis torque, and the torque arm is relatively long. This is the most common installation method.

imgimg

By comparing with traditional vehicles, we can find that when ordinary vehicles move, the wheels all turn in one direction, while when a vehicle using McDonnell Douglas wheels moves omnidirectionally, as analyzed above, the movement direction of each McDonnell Douglas wheel is different. Therefore, if the McDonnell Douglas wheels want to achieve true omnidirectional movement, each McDonnell Douglas wheel needs a separate motor to drive it. In this way, a control system is also required to control the steering and speed of each wheel.

Omnidirectional wheels and Mecanum wheels are commonly used to achieve omnidirectional motion. The above mainly describes the motion methods and installation combinations of Mecanum wheels. Omnidirectional wheels and Mecanum wheels differ in structure, mechanical properties, and kinematic characteristics. The fundamental reason for this is the different angles between the hub axis and the roller axis. The following table summarizes the differences in their kinematic and mechanical properties.

img

The calculation process is as follows, for reference only:

img

8. Final installation method

img

PID control of motor speed
Read IMU data

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