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ROSMASTER X3 Plus📖
1.Preparation and First Trial
  • 1.1 Wiring Introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
  • 1.4 Precautions for DIY Robotic Arm
2. Assembly steps
  • Installation Video
3.Remote control course
  • 3.1 APP remote control
  • 3.2 Model aircraft remote control
  • 3.3 Handle remote control
4.Hardware course
  • 4.1 About expansion board_V3.0
  • 4.1 About expansion board_V1.0
  • 4.2 STM32 Development
  • 4.3 Button to control the buzzer
  • 4.4 FreeRTOS application
  • 4.5 Serial communication
  • 4.6 SBUS model aircraft remote control
  • 4.7 CAN bus communication
  • 4.8 RGB colorful light bar
  • 4.9 Timer interrupt to control
  • 4.10 Control serial port servos
  • 4.11 Nine-axis attitude sensor
  • 4.12 Control motor
  • 4.13 Timer captures the encoder data
  • 4.14 Kinematic Analysis
  • 4.15 PID control robot movement
5.Basic course
  • 5.1 Update the firmware
  • 5.2 Turn off the self-starting process
  • 5.3 Install the Rosmaster driver
  • 5.4 Buzzer whistle
  • 5.5 Control PWM servo
  • 5.6 RGB colorful light bar
  • 5.7 Control motor
  • 5.8 Control robot movement
  • 5.9 Control Serial Servo
6.Linux operating system
  • 6.1 Virtual machine installation
  • 6.2 Linux basics
  • 6.3 Remote control
  • 6.4 Multi-machine communication
  • 6.5 Static IP and hotspot mode
  • 6.6 Bind the device ID
  • 6.7 Web page real-time monitor
  • 6.8 Expansion tutorial
  • 6.9.1 Jetson Nano write sytem
  • 6.9.2 Jetson Xavier NX write system
  • 6.9.3 Jetson TX2 NX write system
  • 6.9.4 Raspberry Pi write system
7.Docker-Just for pi5
  • 7.1 Docker overview and docker installation
  • 7.2 Common commands for docker image containers
  • 7.3 Docker images deeply understand and publish images
  • 7.4 Docker hardware interaction and data processing
  • 7.5 Enter the bot's docker container
  • 7.6 Robot development environment construction in Docker
  • 7.7 How customers can update docker images in the future
8.ROS basic course
  • 8.1 ROS introduction
  • 8.2 ROS installation
  • 8.3 ROS common command tools
  • 8.4 ROS workspace
  • 8.5 ROS function package
  • 8.6 ROS node
  • 8.7 ROS topic publisher
  • 8.8 ROS topic subscribers
  • 8.9 ROS service client
  • 8.10 ROS service server
  • 8.11 ROS action client
  • 8.12 ROS action server
  • 8.13 ROS custom message reception
  • 8.14 ROS-launch file
  • 8.15 ROS-TF transformation
  • 8.16 ROS parameter service
  • 8.17 ROS-rviz use
  • 8.18 ROS-rqt tool usage
  • 8.19 Topic message recording and playback
  • 8.20 urdf model introduction
  • 8.21 gazebo introduction
  • 8.22 ROS distributed communication
9.OpenCV courses
  • 9.1 Getting started with Open Source CV
  • 9.2 Geometric transformation
  • 9.3 Image processing and draw
  • 9.4 Image beautification
  • 9.5 Opencv application
  • 9.6 AR vision
  • 9.7 AR QR code
  • 9.8 ROS+Opencv foundation
  • 9.9 ROS+Opencv application
  • 9.10 Data transformation
  • 9.11 MediaPipe development
10.Robot control course
  • 10.1 PID algorithm theory
  • 10.2 Robot PID debugging
  • 10.3 Robot information release
  • 10.4 Robot keyboard control
  • 10.5 Robot handle control
  • 10.6 Robot state estimation
  • 10.7 Robot calibration
  • 10.8 Robot URDF model
11.Depth camera course
  • 11.1 How to use the Astra camera
  • 11.2 Astra camera calibration
  • 11.3 Astra Color Tracking
  • 11.4 Astra Object Tracking
  • 11.5 Visual tracking autopilot
  • 11.6 Pure visual 2D mapping
  • 11.7 RTAB-Map 3D mapping
  • 11.8 ORB_SLAM2 basics
  • 11.9 ORB_SLAM2_Octomap
12.Lidar course
  • 12.1 Lidar basic-SLAM
  • 12.1 Lidar basic-EAI
  • 12.2 Lidar avoiding
  • 12.3 Lidar guard
  • 12.4 Lidar follow
  • 12.5 Patrol game
  • 12.6 gmapping mapping algoritm
  • 12.7 hector mapping algorithm
  • 12.8 karto mapping algorithm
  • 12.9 cartographer mapping
  • 12.10 rrt_exploration mapping
  • 12.11 AMCL
  • 12.12 Navigation and avoiding
  • 12.13 APP mapping and navigation
13.Multi-robot control course
  • 13.1 Multi-machine handle control
  • 13.2 Multi-machine navigation
  • 13.3 Multi-machine formation
  • 13.4 Multi-machine surround
14.Deep learning courses
  • 14.1 KNN recognizes handwritten
  • 14.2 Basic use of TensorFlow
  • 14.3 Basic use of PyTorch(jetson)
  • 14.4 yolov5 model training(jetson)
  • 14.5 yolov5+tensorrt acceleration
  • 14.6 yolov4-tiny
15.Voice control courses
  • 0. How to use old voice module in new image
  • 15.1 Introduction of voice control-new
  • 15.2 Voice control module port-new
  • 15.3.Introduction of voice module-old
  • 15.4.Voice control module port binding-old
  • 15.5 Voice control car movement
  • 15.6 Voice control Autopilot
  • 15.7 Voice control color recogzition
  • 15.8 Voice control color tracking
  • 15.9 Voice control multi-point nagavation
16.Robotic arm control course
  • 16.1 MoveIt configuration
  • 16.2 MoveIt control the real machine
  • 16.3 MoveIt moves randomly
  • 16.4 MoveIt kinematics design
  • 16.5 MoveIt cartesian path
  • 16.6 MoveIt avoiding
  • 16.7 MoveIt scene design
  • 16.8 MoveIt trajectory planning
  • 16.9 Mediapipe palm control car
  • 16.10 Mediapipe gesture control robotic arm
  • 16.11 Mediapipe gesture control car
  • 16.12 Mediapipe arm attitude control robotic arm
  • 16.13 Mediapipe recognizes and tracks palm control robotic arm
  • 16.14 Recognizes color block and handling
  • 16.15 Autopilot to clear obstacles
17.Voice control robotic arm course
  • 17.1 Voice control robotic arm movement
  • 17.2 Voice control robotic arm learning
  • 17.3 Voice control color block transport
  • 17.4 Voice control robotic arm grip objects
  • 17.5 Voice control robotic arm garbage sorting
18.Model training Autopilot
  • 18.0 ReadMe
  • 18.1 yolov5 to train traffic signs
  • 18.2 TensorRt acceleration identification mark
  • 18.3 Road sign indication function description
  • 18.4 Collect data
  • 18.5 Training model
  • 18.6 Model transformation and application
  • 18.7 AI Autopilot
19.For Orin-Remote control course
1. APP control
  • 1. APP control
  • 2. Model aircraft control
  • 3. Handle control
2. Model aircraft control
3. Handle control
20.Orin-Hardware course
  • 1. About expansion board_V1.0
  • 1. About expansion board_V2.0
  • 2. STM32 Development environment
  • 3. Button control
  • 4. FreeRTOS application
  • 5. Serial communication
  • 6. SBUS model aircraft remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt to control PWM servo
  • 10. Control serial port servos
  • 11. Nine-axis attitude sensor to obtain data
  • 12. Control motor
  • 13. Timer captures the encoder data
  • 14. Robot kinematics analysis theory
  • 15. PID control robot movement
21.Orin-Basic course
  • 1. Update the firmware
  • 2. Turn off the self-starting process at startup
  • 3. Install the Rosmaster driver library
  • 4. Buzzer whistle
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects display
  • 7. Control motor
  • 8. Control robot movement
  • 9. Control Serial Servo
22.Orin-Linux operating systemnew
  • 1. Virtual machine installation and use
  • 2. Linux basics
  • 3. Remote control
  • 4. Multi-machine communication configuration
  • 5. Static IP and hotspot mode
  • 6. Bind the device ID
  • 7. Web page real-time monitoring
  • 8. Expansion tutorial
  • 9.1 Jetson Nano write sytem
  • 9.2 Jetson Xavier NX write system
  • 9.3 Jetson TX2 NX-Orin series write system
  • 9.4 Raspberry Pi write system
  • 10.Jetpack old system upgraded to SUPER system
23.Orin-Dockernew
SUPER image uses ros environment in docker
  • 1、Docker overview and docker installationnew
  • 2、Common commands for docker image containersnew
  • 3、Docker images deeply understand and publish imagesnew
  • 4、Docker hardware interaction and data processingnew
  • 5、Enter the bot's docker containernew
  • 6、Robot development environment construction in Dockernew
23.Orin-ROS basic course
  • 1.ROS introduction
  • 2.Project file structure
  • 3.Common commands and tools
  • 4.Publisher
  • 5.Subscribers
  • 6.Customize topic messages and use
  • 7.Client
  • 8.Server
  • 9.Custom service messages and usage
  • 10.TF release and monitoring
24.Orin-OpenCV courses
  • 1. Getting started with Open 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
  • 5. Opencv application
  • 6. AR vision
  • 7. AR QR code
  • 8. ROS+Opencv foundation
  • 9. ROS+Opencv application
  • 10. Data transformation and point cloud
  • 11. MediaPipe development
25.Orin-Robot control course
  • 1.PID algorithm theory
  • 2.Robot PID debugging
  • 3.Robot information release
  • 4.Robot keyboard control
  • 5.Robot handle control
  • 6.Robot state estimation
  • 7.Robot calibration
  • 8.Robot URDF model
26.Orin-Depth camera course
  • 01.How to use the Astra camera
  • 02.Astra camera calibration
  • 03.Astra Color Tracking
  • 04.Astra Object Tracking
  • 05.Visual tracking autopilot
  • 06.Pure visual 2D mapping and navigation
  • 07.RTAB-Map 3D mapping navigation
  • 08.ORB_SLAM2 basics
  • 09.ORB_SLAM2_Octomap
27.Orin-Lidar course
  • 1.Lidar basic-4ROS-EAI
  • 1.Lidar basic-SLAM
  • 2.Lidar avoiding
  • 3.Lidar guard
  • 4.Lidar follow
  • 5.Patrol game
  • 6.gmapping mapping algorithm
  • 7.hector mapping algorithm
  • 8.karto mapping algorithm
  • 9.cartographer mapping algorithm
  • 10.rrt_exploration mapping algorithm
  • 11.AMCL
  • 12.Navigation and avoiding
  • 13.APP mapping and navigation
28.Orin-Multi-robot control course
  • 1.Multi-machine handle control
  • 2.Multi-machine navigation
  • 3.Multi-machine formation
  • 4.Multi-machine surround
29.Orin-Deep learning courses
  • 1.KNN recognizes handwritten digits
  • 2.Basic use of TensorFlow
  • 3.Basic use of PyTorch(jetson)
  • 4.YOLOv11Model conversion(SUPER)new
  • 5.YOLOv11Dataset annotation(SUPER)new
  • 6.YOLOv11Model conversion + tensorrt acceleration(SUPER)new
  • 7.YOLOv11Object recognition(SUPER)new
30.Orin-Voice control courses
  • 0. How to use old voice module in new image
  • 1.1 Introduction of voice module-new
  • 1.2 Introduction of voice module-old
  • 2.1 Voice control module port binding-new
  • 2.2 Voice control module port binding-old
  • 3. Voice control car movement
  • 4. Voice control Autopilot
  • 5. Voice control color recognition
  • 6. Voice control color tracking
  • 7. Voice control multi-point navigation
31.Orin-Robotic arm control course
  • 1.MoveIt configuration(VM)
  • 2.MoveIt control the real machine(VM)
  • 3.MoveIt moves randomly(VM)
  • 4.MoveIt kinematics design(VM)
  • 5.MoveIt cartesian path(VM)
  • 6.MoveIt avoiding(VM)
  • 7.MoveIt scene design(VM)
  • 8.MoveIt trajectory planning(VM)
  • 9.Mediapipe palm control car
  • 10.Mediapipe gesture control robotic arm
  • 11.Mediapipe gesture control car
  • 12.Mediapipe arm attitude control robotic arm
  • 13.Mediapipe recognizes and tracks palm control robotic arm
  • 14.Recognizes color block and handling
  • 15.Autopilot to clear obstacles
32.Orin-Voice control robotic arm course
  • 1.Voice control robotic arm movement
  • 2.Voice control robotic arm learning
  • 3.Voice control color block transport
  • 4.Voice control robotic arm grip objects
  • 5.Voice control robotic arm garbage sorting
LLM tutorial
1.Development environment
  • 0. Model usage instructions
  • 1.Environments deployment
  • 2.Dialogue platform installation
2.Jetson Nano B01
  • 1.Qwen2 model
  • 2.TinyLlama model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.Orca Mini model
  • 6.StarCoder2 model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA-Phi3
3.Jetson Orin NXNANO
  • 1.Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Codellama model
  • 7.DeepSeek-R1 model
  • 8.Orca Mini model
  • 9.StarCoder2 model
  • 10.TinyLlama model
  • 11.Expand LLaVA
  • 12.Expand LLaVA-Phi3
4.Raspberry Pi 5B
  • 1.Meta AI Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Meta AI Codellama model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA
FAQ
  • Using your own Jetson board, car cannot start normally
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • SystemFile_OrinNANOSUPER
  • SystemFile_OrinNXSUPER
  • SystemFile_RPi_NANO
  • Instruction Manual
  • Code
  • Autopilot Annex
  • VM software
  • Jetson_Boot_USB.tar.gz
  • Other_Annex_File
Download ZIP
FAQ
  • 1、How to connect OLED?
  • 2、Precautions for DIY Robotic Arm

Welcome to ROSMASTER X3 Plus repository

6.gmapping mapping algorithm

6.gmapping mapping algorithm

6.gmapping mapping algorithm6.1 Introduction 6.2 Use 6.2.1 Start 6.2.2 Control the robot 6.2.3 Map save 6.2 Topics and Services 6.3 configuration parameters 6.4 TF transformation

Gmapping: http://wiki.ros.org/gmapping/

map_server: https://wiki.ros.org/map_server

6.1 Introduction

  • gmapping is only applicable to points with less than 1440 2D laser points in a single frame. If the number of laser points in a single frame is greater than 1440, there will be a problem like [[mapping-4] process has died].
  • Gmapping is a common open source SLAM algorithm based on the filtering SLAM framework.
  • Gmapping is based on the RBpf particle filter algorithm, and the real-time positioning and mapping process are separated, and the positioning is carried out first and then the mapping is carried out.
  • Gmapping makes two major improvements to the RBpf algorithm: improved proposal distribution and selective resampling.

Advantages: Gmapping can build indoor maps in real time, requiring less computation and higher accuracy in building small scene maps.

Disadvantage: As the scene grows, the number of particles required increases, because each particle carries a map, so the memory and computation required to build a large map will increase. Therefore, it is not suitable for building large scene maps. And there is no loopback detection, so the map may be dislocated when the loopback is closed. Although increasing the number of particles can make the map closed, it comes at the cost of increasing computation and memory.

image-20220224141102272

6.2 Use

Note: When building a map, the slower the speed, the better the effect(note that if the rotation speed is slower), the effect will be poor if the speed is too fast.

According to different models, you only need to set the purchased model in [.bashrc], X1(ordinary four-wheel drive) X3(Mike wheel) X3plus(Mike wheel mechanical arm) R2(Ackerman differential) and so on. Section takes X3 as an example

Open the [.bashrc] file

sudo vim .bashrc 

Find the [ROBOT_TYPE] parameter and modify the corresponding model

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export  ROBOT_TYPE=X3    # ROBOT_TYPE: X1 X3 X3plus R2 X7 

6.2.1 Start

Start command(robot side), for the convenience of operation, this section takes [mono + laser + yahboomcar] as an example.

​x
#You need to enter docker first, perform this step more
#If running the script to enter docker fails, please refer to 07.Docker-orin/05, Enter the robot's docker container
~/run_docker.sh
roslaunch  yahboomcar_nav  laser_bringup.launch            # laser + yahboomcar 
roslaunch  yahboomcar_nav  laser_usb_bringup.launch        # mono + laser + yahboomcar 
roslaunch  yahboomcar_nav  laser_astrapro_bringup.launch   # Astra + laser + yahboomcar 

Mapping command(robot side)

<Open another terminal and enter the same docker container

image-20240408144126098

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roslaunch  yahboomcar_nav  yahboomcar_map.launch  use_rviz:=false  map_type:=gmapping 
  • [use_rviz] parameter: whether to enable rviz visualization.
  • [map_type] parameter: set the mapping algorithm [gmapping].

Open the visual interface(virtual machine side)

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roslaunch yahboomcar_nav view_map.launch 

image-20220224143306896

The gap at the back of the robot is due to the occlusion of the installation position of the display screen, so a certain range of Lidar data is shielded. The shielding range can be adjusted or not shielded according to the actual situation. For details, see [01. Lidar Basic Course].

6.2.2 Control the robot

  • The keyboard controls the movement of the robot
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roslaunch yahboomcar_ctrl yahboom_keyboard.launch        # custom 
  • The handle controls the movement of the robot

Make the robot walk all over the area to be built, and the map is as closed as possible.

There may be some scattered points during the mapping process. If the mapping environment is well closed and regular, the movement is slow, and the scattering phenomenon is much smaller.

6.2.3 Map save

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rosrun map_server map_saver -f ~/yahboomcar_ws/src/yahboomcar_nav/maps/my_map     # The first way 
bash ~/yahboomcar_ws/src/yahboomcar_nav/maps/map.sh                               # second way 

The map will be saved to ~/yahboomcar_ws/src/yahboomcar_nav/maps/ folder, a pgm image, a yaml file.

map.yaml

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image :  map.pgm 
resolution :  0.05   
origin : [-15.4,-12.2,0.0]  
negate :  0 
occupied_thresh :  0.65 
free_thresh:  0.196 

Parameter parsing:

  • image: The path of the map file, which can be an absolute path or a relative path
  • resolution: the resolution of the map, m/pixel
  • origin: The 2D pose(x, y, yaw) of the lower left corner of the map, where yaw is rotated counterclockwise(yaw=0 means no rotation). Many parts of the system currently ignore the yaw value.
  • negate: whether to reverse the meaning of white/black, free/occupy(the interpretation of the threshold is not affected)
  • occupied_thresh: Pixels with an occupancy probability greater than this threshold will be considered fully occupied.
  • free_thresh: Pixels with an occupancy probability less than this threshold will be considered completely free.

6.2 Topics and Services

Subscribe to topicstypedescribe
tftf/tfMessageUsed to convert between lidar coordinate system, base coordinate system, and odometer coordinate system
scansensor_msgs/LaserScanLidar scan data
Post a topictypedescribe
map_metadatanav_msgs/MapMetaDataPublish map Meta data
mapnav_msgs/OccupancyGridPublish map raster data
~entropystd_msgs/Float64Publish an estimate of the robot pose distribution entropy
Servetypedescribe
dynamic_mapnav_msgs/GetMapGet map data

Node view

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rqt_graph 

image-20220224141102272

6.3 configuration parameters

parametertypeDefaultsdescribe
~throttle_scansint1After receiving the laser data of this number of frames, only one frame of data is processed. By default, it is processed every time a frame of data is received.
~base_framestring"base_link"Robot base coordinate system
~map_framestring"map"map coordinate system
~odom_framestring"odom"Odometer Coordinate System
~map_update_intervalfloat5.0The map update frequency, the lower the value, the greater the computational load
~maxUrangefloat80.0The maximum range that the laser can detect
~sigmafloat0.05Standard Deviation of Endpoint Match
~kernelSizeint1Find in the corresponding kernel
~lstepfloat0.05Optimization step size during translation
~astepfloat0.05Optimization step size during rotation
~iterationsint5The number of iterations to scan for matches
~lsigmafloat0.075Laser Standard Deviation for Likelihood Calculations
~ ogainfloat3.0Used to smooth the resampling effect when calculating the likelihood
~ lskipint0Number of beams skipped per scan
~ minimumScorefloat0Scan the lowest value for matching results. Avoid jumping pose estimation in large open spaces when using laser scanners with limited range(e.g. 5m)
~srrfloat0.1translation function(rho/rho), odometer error while translation
~srtfloat0.2Rotation function(rho/theta), odometry error in translation
~strfloat0.1Translation function(theta/rho), odometry error while rotating
~sttfloat0.2Rotation function(theta/theta), odometer error while rotating
~linearUpdatefloat1.0After the robot translates this distance, the laser scan data is processed once
~angularUpdatefloat0.5The laser scan data is processed every time the robot rotates this distance
~temporalUpdatefloat-1.0If the latest scan is being processed slower than the update, then a scan is processed. A negative value turns off time-based updates
~resampleThresholdfloat0.5Resampling threshold based on Neff
~particlesint30number of particles in the filter
~xminfloat-100.0map x to initial minimum size
~yminfloat-100.0Map y to initial minimum size
~xmaxfloat100.0map x to initial maximum size
~ymaxfloat100.0The initial maximum size of the map y direction
~deltafloat0.05map resolution
~llsamplerangefloat0.01Shifted sampling distance for likelihood calculation
~llsamplestepfloat0.01Shifted sampling step size for likelihood calculation
~lasamplerangefloat0.005Rotation sampling distance for likelihood calculation
~lasamplestepfloat0.005Rotation sampling step size for likelihood calculation
~transform_publish_periodfloat0.05The time interval at which the TF transform is published
~occ_threhfloat0.25Threshold for raster map occupancy
~maxRange(float)float-maximum range of the sensor

6.4 TF transformation

Required TF Transformdescribe
laser-->base_linkThe transformation between the lidar coordinate system and the base coordinate system, generally published by robot_state_publisher or static_transform_publisher
base_link-->odomThe transformation between the map coordinate system and the robot's odometer coordinate system to estimate the robot's pose in the map
Published TF Transformdescribe
map-->odomThe transformation between the map coordinate system and the robot's odometer coordinate system to estimate the robot's pose in the map

View tf tree

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rosrun rqt_tf_tree rqt_tf_tree 

image-20220224141102272

Patrol game
hector mapping algorithm

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