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ROSMASTER M3 PRO📖
0.Configuration and Operation Guide
  • 1. Quick start handle to control the car
  • 2. Log in to the car and view the code
  • 3. Robotic Arm Calibration
  • 4. Enter the Docker (Jetson-Nano and Raspberry Pi 5 users, see here)
  • 5. Assembly video
  • 6. Modify the firmware parameters of the car control board
  • 7. Frequently Asked Questions and Precautions
  • 8. Precautions for DIY Robotic Arm
1.Chassis Control Course
  • 1.ROS control
  • 2.Handle control
  • 3.Keyboard control
  • 4.Angular velocity calibration
  • 5.Line velocity calibration
  • 6.Line patrol automatic driving
  • 7.Patrol
2.Lidar Course
  • 1.Lidar introduction and use
  • 2.Dual Lidar fusion
  • 3.Lidar obstacle avoidance
  • 4.Lidar tracking
  • 5.Lidar guard
  • 6.Gmapping-SLAM mapping
  • 7.Cartographer-SLAM mapping
  • 8.slam_toolbox mapping
  • 9.Navigation2 single-point navigation avoid
  • 10.Navigation2 multi-point navigation avoid
  • 11.Repositioning navigation
  • 12.RTAB-Map mapping
  • 13.RTAB-Map navigation
  • 14.APP mapping navigation
  • 15.Machine code handling
  • 16.Color block transport
  • 17.Navigation via Pose-Constrained Visual Relocalizationnew
3.Depth Camera Course
  • 1.Dabai_DCW2 camera introduction
  • 2.Depth pseudo-color image
  • 3.Depth camera distance measurement
  • 4.Wood block volume measurement
  • 5.Mediapipe gesture recognition
  • 6.Edge detection
  • 7.YOLOv8 object detection
  • 8.Deep learning object tracking
  • 9. Transformer Model Tracking Algorithmnew
4.Robotic Arm and 3D Space Gripping Course
  • 1.Robotic arm solution
  • 2.Robotic arm chassis linkage control
  • 3.Machine code ID sorting
  • 4.Sorting height abnormality machine code
  • 5.Tracking and gripping machine code
  • 6.Color block color sorting
  • 7.Sorting height abnormality color block
  • 8.Tracking and gripping color block
  • 9.Wood block shape sorting
  • 10.KCF tracking and gripping objects
  • 11.Medipipe gesture ID sorting machine code
  • 12.Mediapipe gesture height sorting machine code
  • 13.Desktop tracking and gripping machine code
  • 14.3D tracking machine code
  • 15.Line patrol and obstacle removal
5.Road network planning and navigationnew
  • 1. Introduction To Road Network Planning
  • 2. Build A Pose Map
  • 3. Road Network Annotation
  • 4. Planning And Navigation Between Waypoints
  • 5. Road Network Navigation In Any Pose
  • 6. Path Re-planning
6.OpenClaw Basic Coursenew
  • 1. Introduction to OpenClaw
  • 2. OpenClaw Core Concepts
  • 3. OpenClaw API-KEY Configuration
  • 4. OpenClaw Switch Models
  • 5. OpenClaw System Prompt Words
  • 6. OpenClaw Common CLI Commands
7.OpenClaw Interaction Methodnew
  • 1. OpenClaw Webchat Interaction
  • 2. OpenClaw Tui Interaction
  • 3. OpenClaw Voice Dialogue Interaction
  • 4. OpenClaw WhatsApp Interaction
8.OpenClaw Developmentnew
  • 1. OpenClaw connects to the MCP interface of the robot
  • 2. Robot CLI Command Tool
  • 3. Robot control plugin tool
  • 4. Reply bridge plugin tool
  • 5. OpenClaw multimodal vision
  • 6. OpenClaw chassis control
  • 7. OpenClaw robotic arm control
  • 8. OpenClaw skills development
  • 9. OpenClaw timed tasks
  • 10. OpenClaw multi-agent intelligent agent
  • 11. OpenClaw active interaction
  • 12. OpenClaw vector memory
9.OpenClaw Embodied intelligent applicationnew
  • 1. OpenClaw target tracking
  • 2. OpenClaw robotic arm tracking and grasping
  • 3. OpenClaw robotic arm fixed-point placement
  • 4. OpenClaw garbage sorting
  • 5. OpenClaw slam mapping and navigation
  • 6. OpenClaw navigation transfer
  • 7. OpenClaw machine code clip
  • 8. OpenClaw color line patrol
  • 9. OpenClaw visual distance measurement
10.OpenClaw scenario sandboxnew
  • 1. Map description and prop placement
  • 2. Build sand table map
  • 3. Visual relocation markers
  • 4. Map mapping markers
  • 5. OpenClaw scenario sandbox comprehensive case
11.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
12.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
13.AI Large Model (Text Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
14.AI Large Model(Voice Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
15.Mediapipe Visual Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Facial Landmark Detection
  • 5.Face detection
  • 6.Face special effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Palm target positioning
  • 11.Fingertip trajectory recognition
  • 12.Fingertip gesture control robotic arm
  • 13.Gesture grabbing and releasing objects
  • 14.Finger control robotic arm
  • 15.Medipipe gesture control robotic arm action group
16.MoveIt2 Simulation Course
  • 1.MovelT2 configuration
  • 2.MovelT2 simulation-reality linkage
  • 3.Random movement
  • 4.Forward kinematics design
  • 5.Inverse kinematics design
  • 6.Cartesian path
  • 7.Trajectory planning
  • 8.Collision detection
  • 9.Scene design
17.Multi-vehicle Course
  • 1.Multi-vehicle chassis control
  • 2.Multi-vehicle robotic arm control
  • 3.Multi-vehicle navigation
  • 4.Multi-vehicle formation
18.Control Board Course
  • 1.Development environment setup
  • 2.Control board STM32 basic routine
  • 3.Control board micro_ros basic routine
19.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
20.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
21.Main Control Course
Jetson Nano B01
  • 1. Introduction to the Jetson Nano B01 SUB Board
  • 2. Burn the system image
  • 3. Re-read the memory card or USB drive that has been burned
  • 4. Startup system
  • 5. Jetson Nano B01 System and Desktop Introduction
  • 6. Memory card or USB flash drive expansion
  • 7. Network Configuration
  • 8. SSH remote login & file transfer
  • 9. VNC Remote Login
  • 10. Jetson Nano B01 system backup
  • 11. Jetson Nano Swap space increased
  • 12. Installation and use of Jtop
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
22.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
23.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
Download
  • Mapping_APP(iOS_Android)
  • 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 PRO repository

2.Robotic arm chassis linkage control

Robotic arm chassis linkage control

1. Content Description

This course implements the use of an inverse solution algorithm for the robotic arm to calculate the target pose of the robotic arm and control the robotic arm to move to that pose. At the same time, the chassis also moves synchronously, with the robotic arm and chassis moving in tandem relative to each other.

This section requires entering commands in the terminal. The terminal you open depends on your motherboard type. This lesson uses the Raspberry Pi 5 as an example. For Raspberry Pi and Jetson-Nano boards, you need to open a terminal on the host computer and enter the command to enter the Docker container. Once inside the Docker container, enter the commands mentioned in this section in the terminal. For instructions on entering the Docker container from the host computer, refer to this product tutorial [Configuration and Operation Guide]--[Enter the Docker (Jetson Nano and Raspberry Pi 5 users, see here)].

Simply open the terminal on the Orin motherboard and enter the commands mentioned in this section.

2. Program startup

First, open the terminal and enter the following command to start the robot arm solver.

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ros2 run arm_kin kin_srv

In another terminal, enter

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ros2 run M3Pro_demo M3Pro_Dancing

After the program is started, the robot arm will reach the initial posture. Then enter the following command in the terminal to start it:

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ros2 topic pub /start_dancing std_msgs/msg/Bool "data: True"  --once

After posting this topic, the robot will move forward and the robotic arm will move backward. The robot will move forward 1 meter and then stop. If the above message is posted again, the robot will move back 1 meter.

3. Core code analysis

Code path:

  • Raspberry Pi and Jetson-Nano board

    The program code is in the running docker. The path in docker is/root/yahboomcar_ws/src/M3Pro_demo/M3Pro_demo/ M3Pro_Dancing.py

  • Orin Motherboard

    The program code path is/home/jetson/yahboomcar_ws/src/M3Pro_demo/M3Pro_demo/ M3Pro_Dancing.py

Import the used libraries,

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import rclpy
from std_msgs.msg import Bool
import time
import math
from arm_msgs.msg import ArmJoints
from arm_msgs.msg import ArmJoint
from arm_interface.srv import *
import threading
from rclpy.node import Node
from geometry_msgs.msg import Twist

The program initializes and creates publishers and subscribers,

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def __init__(self, name):
    super().__init__(name)
    self.init_joints = [90, 150, 12, 20, 90, 0]
    #Define the array that stores the current end pose coordinates
    self.CurEndPos =[0.11960304060136814, -0.003155561702528526, 0.3441364762143022, -3.151713610590853e-06, -0.03490854071745721, -0.028421869689521588]
    #Create a publisher for topics related to six servo controls
    self.pub_SixTargetAngle = self.create_publisher(ArmJoints, "arm6_joints", 10)
    #Create a subscriber to subscribe to the start topic
    self.sub_start = self.create_subscription(Bool,"/start_dancing",self.startFlagCallBack,100)
    #Create a publisher for the speed topic
    self.CmdVel_pub = self.create_publisher(Twist,"cmd_vel",1)
    #Create a client that calls the robotic arm solution service
    self.client = self.create_client(ArmKinemarics, 'get_kinemarics')
    while not self.client.wait_for_service(timeout_sec=1.0):
        self.get_logger().info('Service not available, waiting again...')       
    #Assign the current servo angle value to the servo angle value of the initial posture
    self.cur_joints = self.init_joints
    #Control the direction variable of the car moving forward and backward
    self.direction = 1
    #Get the current end position of the robotic arm
    self.get_current_end_pos()
    time.sleep(3)
    #CarMotionTime
    self.runtime = 10
    #Car movement speed
    self.vx = 0.1
    self.pubSixArm(self.init_joints)
    print('init done')

The startFlagCallBack callback function processes the message data released. If the message is true, two threads are started to control the operation of the robotic arm and chassis respectively. The parameters passed in are self.direction and -self.direction, which indicate the relative direction of movement.

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def startFlagCallBack(self,msg):
    if msg.data == True:
        base = threading.Thread(target=self.base_move, args=(self.direction,))
        arm = threading.Thread(target=self.arm_move, args=(-self.direction,))
        arm.start()
        base.start()

base_move controls the chassis movement function,

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def base_move(self,base_dir):
    self.vx = self.direction * 0.1
    #Publish movement speed
    self.pubVel(self.vx,0.0,0.0)
    time.sleep(self.runtime)
    #Publish parking speed
    self.pubVel(0.0,0.0,0.0)
    #Convert the value of self.direction, the next run will be in the reverse direction
    self.direction = -self.direction

arm_move controls the movement of the robotic arm.

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def arm_move(self,arm_dir):
    print("------------------------------------------------")
    #print("pose_T: ",pose_T)
    #Create service request data
    request = ArmKinemarics.Request()
    #According to the value of arm_dir, increase or decrease in the current x direction. The amount of increase or decrease is the distance the car moves.
    request.tar_x = self.CurEndPos[0] + arm_dir*(abs(self.vx)*self.runtime)/10
    #y direction remains consistent
    request.tar_y = self.CurEndPos[1] 
    #The value in the z direction is calculated by trigonometric function to obtain the variable, plus the current value
    request.tar_z = math.tan(-0.03490672894389764) * (request.tar_x - self.CurEndPos[0]) + self.CurEndPos[2]
    print("request.tar_x: ",request.tar_x)
    print("request.tar_z: ",request.tar_z)
    #Call the ik inverse solution to calculate the end-of-arm posture coordinates from various angles
    request.kin_name = "ik"
    #rpy value should be consistent with the current
    request.roll = self.CurEndPos[3]
    request.pitch  = self.CurEndPos[4]
    request.yaw = self.CurEndPos[5]
    print("calcutelate_request: ",request)
    #The client calls the get_kinemarics service
    future = self.client.call_async(request)
    future.add_done_callback(self.get_ik_respone_callback)

get_ik_respone_callback receives the callback function that returns the result of calling the ik service.

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def get_ik_respone_callback(self, future):
    try:
        response = future.result()
        joints = [0.0, 0.0, 0.0, 0.0, 0.0,0.0]
        #Assign values to servos 1-3. The assigned values are the values of the responses returned after the service is processed.
        joints[0] = int(response.joint1) #response.joint1
        joints[1] = int(response.joint2)
        joints[2] = int(response.joint3)
        joints[3] = int(response.joint4)
        joints[4] = 90
        joints[5] = 30
        print("compute_joints: ",joints)
        self.cur_joints = joints
        #Publish a topic about controlling the angles of six servos
        self.pubSixArm(joints)
        time.sleep(1.5)
    except Exception as e:
        self.get_logger().error(f'Service call failed: {e}')

get_current_end_pos gets the current end position function of the robotic arm.

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def get_current_end_pos(self):
    #Create service request data
    request = ArmKinemarics.Request()
    #Assign values to cur_joint1 to cur_joint5 in the request data. The assigned values are the joint angle values of the current robot arm.
    request.cur_joint1 = float(self.cur_joints[0])
    request.cur_joint2 = float(self.cur_joints[1])
    request.cur_joint3 = float(self.cur_joints[2])
    request.cur_joint4 = float(self.cur_joints[3])
    request.cur_joint5 = float(self.cur_joints[4])
    #Call fk to calculate the end-of-arm posture coordinates from various angles
    request.kin_name = "fk"
    future = self.client.call_async(request)
    future.add_done_callback(self.get_fk_respone_callback)

get_fk_respone_callback receives the callback function that returns the result of calling the fk service.

x
def get_fk_respone_callback(self, future):
    try:
        #Receive the result returned after calling the service
        response = future.result()
        #self.get_logger().info(f'Response received: {response.x}')
        #Assign a value to self.CurEndPos. The value assigned is the response data, including the six values of xyz and rpy.
        self.CurEndPos[0] = response.x
        self.CurEndPos[1] = response.y
        self.CurEndPos[2] = response.z
        self.CurEndPos[3] = response.roll
        self.CurEndPos[4] = response.pitch
        self.CurEndPos[5] = response.yaw
        #self.get_logger().info(f'Response received: {self.CurEndPos}')
        print("self.CurEndPose: ",self.CurEndPos)
    except Exception as e:
        self.get_logger().error(f'Service call failed: {e}')

 

Robotic arm solution
Machine code ID sorting

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