yahboom
HomeLearn
Solution
Blog
Online Store
About Us
EN/中文
Muto RS Robot📖
01.Introduction to Muto Robot
  • 1.Accessories introduction
  • 2.Precautions for battery
  • 3.FAQ
  • 4.MutoRS Study Guide
  • 5.Quick Start Guide
02.Assembly and write system
  • 1.Raspberry Pi write system
  • 2.Assembly video
03.Remote control course
  • 1.Open and Close APP Control Program
  • 2.Mobile APP Remote Control
  • 3.USB Wireless Controller Remote Control
  • 4.Web Host Computer Remote Control
04.Remote login operation
  • 1.SSH and VNC remote login
  • 2.Jupyter Lab login and running example code
05.AI Large Model Basicsnew
  • 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
06.AI Large Model Developmentnew
  • 1.Register a model service provider account
  • 2.Configure API-KEY
  • 3.Introduction to Dify
  • 4.Dify Basic function
  • 5.AI large model development - Chatbot
  • 6.Deploy the RAG knowledge base
  • 7.RAG knowledge base + Chatbot
  • 8.AI agent workflow
  • 9.Custom wake-up response
  • 10.Core module testing tools
07.AI Large Model (Text Version)new
  • 0.MutoRS Model Setup & Details
  • 1.Semantic Understanding, Instruction Following
  • 2.Multimodal Visual Understanding
  • 3.Multimodal Visual Understanding + Depth Camera Distance Q&A
  • 4.Multimodal Visual Understanding + Visual Line Following
  • 5.Multimodal Visual Understanding + Visual Following
  • 6.Multimodal Visual Understanding + SLAM Navigation
  • 7.Multimodal Visual Understanding + SLAM Navigation + Visual Line Following
  • 8.Intention Estimation + Multimodal Visual Understanding + SLAM Navigation + Visual Functions
08.AI Large Model (Voice Version)new
  • 0.MutoRS Model Setup & Details
  • 1.Semantic Understanding, Instruction Following
  • 2.Multimodal Visual Understanding
  • 3.Multimodal Visual Understanding + Depth Camera Distance Q&A
  • 4.Multimodal Visual Understanding + Visual Line Following
  • 5.Multimodal Visual Understanding + Visual Following
  • 6.Multimodal Visual Understanding + SLAM Navigation
  • 7.Multimodal Visual Understanding + SLAM Navigation + Visual Line Following
  • 8.Intention Estimation + Multimodal Visual Understanding + SLAM Navigation + Visual Functions
09.AI Large Model (Fun Applications)new
  • 1.Versatile performer
  • 2.Action Imitation
  • 3.Problem Solving Expert
  • 4.Exploring Unfamiliar Environments
10.Robot basic course
  • 1)Control buzzer
  • 2)Control PWM servo
  • 3)Control bus servo
  • 4)Robot forward and backward
  • 5)Robot move left and right
  • 6)Robot rotate left and right
  • 7)Control height
  • 8)Control head
  • 9)Action group performance
  • 10)Read data
  • 11)Host computer control
  • 12)Camera driver
11.Voice control coursenew
  • 1.Control Muto motion
  • 2.Control Muto tracking
  • 3.Control Muto color recognition
  • 4.Control Muto color tracking
  • 5.Control multi-point navigation
12.AI vision course
  • 0)Color HSV Calibration
  • 1)Color recognition
  • 2)Color following
  • 3)Color recognition action group
  • 4)Face detection
  • 5)QR code recognition
  • 6)QR code instructions
  • 7)Visual line following
  • 8)Motion learning
  • 9)Synchronous action teaching
13.Mediapipe course
  • 1)Hand detection
  • 2)Attitude detection
  • 3)Overall inspection
  • 4)Face detection
  • 5)Face recognition
  • 6)Face effects
  • 7)Face detection
  • 8)3D object recognition
  • 9)Brush
  • 10)Finger control
  • 11)Gesture recognition
14.Depth camera course
  • 1)Depth camera use
  • 2)Internal reference calibration
  • 3)Web page real-time monitoring
  • 4)ROS+opencv application
  • 5)QR code creation and recognition
  • 6)AR vision
  • 7)Tracking
  • 8)Obstacle recognition
  • 9)Color tracking
  • 10)KCF object tracking
  • 11)RTAB-Map 3D mapping navigation
15.Lidar course
  • 1)About lidar
  • 2)Lidar obstacle avoidance
  • 3)Lidar tracking
  • 4)Lidar guard
  • 5)Patrol
  • 6)SLAM overview
  • 7)ROS2_Gmapping mapping algorithm
  • 8)ROS2_Cartographer mapping algorithm
  • 9)About ROS2 Navigation
  • 10)ROS2 navigation and avoid
  • 11) APP mapping
  • 12)APP navigation
16.Road network navigationnew
  • 1.Road network planning and navigation environment deployment
  • 2.Constructing pose maps
  • 3.Road network annotation
  • 4.Inter-road network planning and navigation
  • 5.Road network navigation under arbitrary poses
  • 6.Path replanning
17.Multi-machine control course
  • 1)Multi-vehicle control
  • 2)Multi-vehicle navigation
18.Robot chassis control
  • 1)Hexapod gait and kinematics
  • 2)Robot information release
  • 3)Robot control
  • 4)URDF model
19.Raspberry Pi course
  • 1)Python usage
  • 2)Use APT to install Python packages
  • 3)Python virtual environment
  • 4)GPIO Zero
  • 5)Pin output high and low levels
  • 6)Pin reads high and low levels
  • 7)Pin output PWM
  • 8)Serial communication
  • 9)I2C communication
20.OpenCV course
  • 1)Introduction to Open Source CV
  • 2)Image reading and display
  • 3)Picture writing
  • 4)Image Quality
  • 5)Pixel operations
  • 6)Image zoom
  • 7)Picture cut
  • 8)Picture pan
  • 9)Picture mirroring
  • 10)Affine transformation
  • 11)Picture rotation
  • 12)perspective transformation
  • 13)Grayscale processing
  • 14)Binary image
  • 15)Edge green detection
  • 16)Line segment drawing
  • 17)Rectangular circle drawing
  • 18)Text and picture drawing
21.Docker course
  • 1)Overview and installation
  • 2)Common commands
  • 3)Deeply understand and publish images
  • 4)Hardware interaction and data processing
  • 5)Enter docker container
  • 6)Robot development environment construction
  • 7)Update docker images
22.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
23.Linux basic 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)Driver library and communication
  • 10)Static IP and hotspot mode
  • 11)Bind device ID
  • 12)Capacity expansion and resource allocation
  • 13)Back up system image
  • 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
24.Mainboard Large Model Course
Offline large model (Text Interaction)
  • 0. AI large model image description
  • 1. Deployment of large AI model environments
  • 2. Installation of large model dialogue platform
  • 3. Meta AI Llama 3.2 model
  • 4. Alibaba Cloud Qwen3 Model
  • 5. Microsoft Phi-4-mini model
  • 6. DeepSeek DeepSeek-R1 model
  • 7. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 8. Google Gemma3 Visual Multimodal Large Model
  • 9. Llava Visual Multimodal Large Model
  • 10. MiniCPM-V Visual Multimodal Large Model
  • 11. Multimodal Visual Understanding Applications
  • 12. Multimodal Text-Based Image Application
  • 13. Multimodal Video Analysis Application
  • 14. Multimodal visual positioning applications
  • 15. Multimodal table scanning application
  • 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
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • SystemFile_For_RPi5
  • SystemFile_Jetson
  • SystemFile_RPi
  • Code
  • VM File
  • Hardware Info
  • Instruction Manual
  • Jetson_Boot_USB.tar.gz
  • ROS2_Basic_VM
Download ZIP

Welcome to Muto RS Robot repository

3)Robot control

3.Robot control

 

1. Program function description

Turn on the chassis and run the handle/keyboard control program. You can control Muto movement through the handle or keyboard. The handle also has functions such as controlling the buzzer.

2. Program code reference path

After entering the docker container, the source code of the handle control function is located at

/root/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_ctrl/yahboomcar_ctrl/yahboom_joy.py

After entering the docker container, the source code of the keyboard control function is located at

xxxxxxxxxx
/root/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_ctrl/yahboomcar_ctrl/yahboom_keyboard.py

3. Program startup

xxxxxxxxxx
#Low-level driver
ros2 run yahboomcar_bringup muto_driver
#Handle control
ros2 launch yahboomcar_ctrl yahboomcar_joy_launch.py
#keyboard control
ros2 run yahboomcar_ctrl yahboom_keyboard

Note:

1. The handle and keyboard cannot run at the same time, because after keyboard control is started, when the keyboard is not pressed, a speed message of 0 data will be sent by default

2. Make sure that the controller device has been mounted into the docker container and can be executed in the docker container:

xxxxxxxxxx
ls /dev/input

image-20230921210334160

If the js0 device exists, it means it has been successfully mounted.

3. After the js0 device has been successfully mounted, do not switch the receiver of the plug-and-play controller to other USB ports of the main control. This will also easily cause the controller to lose connection.

 

3.1. Handle control

After turning it on, press the "START" button and hear the buzzer sound to start remote control. The remote control will enter sleep mode after being turned on for a period of time, and you need to press the "START" button to end sleep. If you want to control the operation of the car, you also need to press the R2 key and release the motion control lock before you can use the joystick to control the movement of the car.

Remote control effect description,

HandleEffect
Left joystick up/downForward/Backward to go straight
Left joystick left/rightLeft/right to go straight
Right joystick left/rightLeft rotation/right rotation
Right "2" keyUnlock/lock motion control
"START" buttonControl buzzer/end sleep
Press the left joystickAdjust the X/Y axis speed
Press the right joystickAdjust the angular velocity

3.2.Keyboard control

Key Description,

  • Directional control

    [i] or [I][linear, 0][u] or [U][linear, angular]
    [,][-linear, 0][o] or [O][linear, - angular]
    [j] or [J][0, angular][m] or [M][- linear, - angular]
    [l] or [L][0, - angular][.][- linear, angular]
  • Speed control

    ButtonSpeed changeButtonSpeed change
    [q]Linear speed and angular speed are both increased by 10%[z]Linear speed and angular speed are both reduced by 10%
    [w]Only the linear speed increases by 10%[x]Only the linear speed decreases by 10%
    [e]Only the angular velocity increases by 10%[c]Only the angular velocity decreases by 10%
    [t]Line speed X-axis/Y-axis direction switching[s]Stop keyboard control

3.3. Node communication

Communication diagram of handle control car node,image-20230808170625594

Keyboard control car node communication diagram,

image-20230419160740283

4. Core code analysis

4.1. Controller code

The chassis control program is muto_driver.py, which defines two subscribers: speed (/cmd_vel) and buzzer (/Buzzer).Therefore, as long as we publish this type of topic data in the controller control code program yahboom_joy.py, we can control the speed and buzzer.

xxxxxxxxxx
#create pub
self.pub_goal = self.create_publisher(GoalID,"move_base/cancel",10)
self.pub_cmdVel = self.create_publisher(Twist,'cmd_vel',  10)
self.pub_Buzzer = self.create_publisher(Bool,"Buzzer",  1)
self.pub_JoyState = self.create_publisher(Bool,"JoyState",  10)

In addition, we need to subscribe to the "joy" topic data, which can tell us that those key values (joysticks and buttons) have changed, that is,

xxxxxxxxxx
#create sub
self.sub_Joy = self.create_subscription(Joy,'joy', self.buttonCallback,10)

The main thing to look at is the callback function of this joy topic. It parses the received value, then assigns it to the publisher's variable, and finally publishes it.

xxxxxxxxxx
def buttonCallback(self,joy_data):
    if not isinstance(joy_data, Joy): return
    if self.user_name == "root": self.user_jetson(joy_data)
    else: self.user_pc(joy_data)

The function jumps here are all self.user_jetson, and the parameter variable passed in is the received topic.

xxxxxxxxxx
def user_jetson(self, joy_data):

Take controlling the buzzer as an example for analysis.

xxxxxxxxxx
if joy_data.buttons[11] == 1:
    Buzzer_ctrl = Bool()
    self.Buzzer_active=not self.Buzzer_active
    Buzzer_ctrl.data =self.Buzzer_active
    for i in range(3): self.pub_Buzzer.publish(Buzzer_ctrl)

Here it is judged that if joy_data.buttons[11] == 1 that is, if "start" is pressed, the value of the buzzer will change and then be published self.pub_Buzzer.publish(Buzzer_ctrl) . The others are deduced by analogy, and the principle is the same. They all assign values by detecting changes in key values. For detailed code, refer to yahboom_joy.py.

 

4.2. Keyboard control code

Keyboard control can only control Muto's motion control, not Muto's buzzer. Therefore, there is only one /cmd_vel speed publisher,

xxxxxxxxxx
self.pub = self.create_publisher(Twist,'cmd_vel',1)

The program also defines two dictionaries to detect changes in keyboard letters when they are pressed.

​x
moveBindings = {
    'i': (1, 0),
    'o': (1, -1),
    'j': (0, 1),
    'l': (0, -1),
    'u': (1, 1),
    ',': (-1, 0),
    '.': (-1, 1),
    'm': (-1, -1),
    'I': (1, 0),
    'O': (1, -1),
    'J': (0, 1),
    'L': (0, -1),
    'U': (1, 1),
    'M': (-1, -1),
}
​
speedBindings = {
    'Q': (1.1, 1.1),
    'Z': (.9, .9),
    'W': (1.1, 1),
    'X': (.9, 1),
    'E': (1, 1.1),
    'C': (1, .9),
    'q': (1.1, 1.1),
    'z': (.9, .9),
    'w': (1.1, 1),
    'x': (.9, 1),
    'e': (1, 1.1),
    'c': (1, .9),
}

Entering the while loop, the program will read the value of the keyboard press, and then make judgments layer by layer.

xxxxxxxxxx
key = yahboom_keyboard.getKey()
if key=="t" or key == "T": xspeed_switch = not xspeed_switch
elif key == "s" or key == "S":
...
if key in moveBindings.keys():
...
elif key in speedBindings.keys():
..

Finally, based on multi-layer judgment, assign values to twist.linear.x, twist.linear.y, twist.angular.z and then publish them.

xxxxxxxxxx
if xspeed_switch: twist.linear.x = speed * x
else: twist.linear.y = speed * x
twist.angular.z = turn * th
if not stop: yahboom_keyboard.pub.publish(twist)
if stop:yahboom_keyboard.pub.publish(Twist())

Detailed code reference : yahboom_keyboard.py

Robot information release
URDF model

COMPANY

  • About us
  • Team Style
  • Blog

LEARNING & EXPLORATION

  • Tutorials
  • Edge AI hardware
  • Educational robots
  • Embedded intelligent hardware

WHERE TO BUY

  • Yahboom Online Store
  • Amazon
  • Alibaba
  • AliExpress
  • Mercado

TECHNOLOGY SUPPORT

  • Email: support@yahboom.com

BE A DISTRIBUTOR

  • Email: sales@yahboom.com
QR Code

Copyright © 2017-2026 Yahboom. All Rights Reserved.

EN/中文