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DOFBOT Pro📖
1. Get started quickly
  • 0.Unboxing introduction video
  • 1. First Trial
  • 2. Analysis of common problems
  • 3. Calibration offset
  • 4. Jetpack old system upgraded to SUPER system
  • 5. Precautions for DIY Robotic Arm
  • 6. Jetson Nano Version – Must Read
  • 7.Robotic arm gripping anomaly solutions
2. Assembly course
  • 1. Installation video
  • 2. Calibrate the Robotic Arm
  • 3. Install the map
3. Remote control course
  • 1. Mobile APP control
  • 2. USB handle control
  • 3. PC web page programming
4. Preparation
  • 1. Manage APP control services
  • 2. Manage JupyterLab services
  • 3. Manage OLED services
  • 4. Modifying ROS_DOMAIN_ID for Multi-Machine
5. 2D visual tracking course
  • 1. Color block positioning
  • 2. Color block tracking
  • 3. Face positioning
  • 4. Face tracking
  • 5. Label code positioning
  • 6. Label code tracking
6. 2D visual grabbing course
  • 1. Identify grab color block
  • 2. Catch game
  • 3. Color sorting
  • 4. Color stacking
  • 5. Label code sorting
  • 6. Label code stacking
  • 7. Identify gesture grab color block
  • 8. Gesture stacking
  • 9.1 Single garbage sorting(Jetson-Nano)
  • 9.2 Single garbage sorting(Orin)
  • 10.1 Garbage sorting(Jetson-Nano)
  • 10.2 Garbage sorting(Orin)
  • 11. Tracking game
7. 2D Mediapipe course
  • 1. Hand detection
  • 2. Posture detection
  • 3. Overall detection
  • 4. Facial Detection
  • 5. Face Detection
  • 6. Face effects
  • 7. 3D Object Recognition
  • 8. Paintbrush
  • 9. Finger control
  • 10. Gesture Recognition
  • 11. Gesture control robotic arm action group
  • 12. Arm posture control robotic arm
  • 13. Palm Targeting
  • 14.1 Robotic arm tracks the palm(Jetson-Nano)
  • 14.2 Robotic arm tracks the palm(Orin)
  • 15. Fingertip trajectory recognition
  • 16. Fingertip trajectory control robotic arm
  • 17. Gesture Grasp and Release Objects
  • 18. Finger Control Robot Arm
8. AI Large Model Voice Module (Must Read)new
  • 1. Introduction to AI Large Model Voice Module
  • 2. Flash the firmware (Must read)
  • 3. Modify the wake-up word and command word
9. 2D Voice Control Course
  • 1.Simple Voice Control
  • 2.Smart Voice Control
  • 3.Voice Action Control
  • 4.Memory Learning Mode
  • 5.Color Recognition with Voice Broadcast
  • 6.Color Recognition and Grasping with Voice Broadcast
  • 7.Color Recognition and Sorting with Voice Broadcast
  • 8.Voice Recognition Target Tracking
  • 9.Voice Control Stacking
  • 10.Voice Control Color Sorting
  • 11.1 Garbage Recognition with Voice Broadcast(Jetson Nano)
  • 11.2 Garbage Recognition with Voice Broadcast(Orin)
  • 12.1 Garbage Sorting with Voice Broadcast(Jetson Nano)
  • 12.2 Garbage Sorting with Voice Broadcast(Orin)
10. 3D Depth Camera Course
  • 1. Dabai_DCW2 camera
  • 2. Deep Pseudo-Color Image
  • 3. Depth camera ranging
  • 4. Color block volume measurement
11. 3D Robotic Arm Control and Inverse Kinematics
  • 1. Robotic arm ROS control
  • 2. Inverse solution
12. 3D Sorting and Grasping Course
  • 1. AprilTag ID sorting
  • 2. AprilTag abnormal height sorting
  • 3. MediaPipe Gesture-AprilTag ID sorting
  • 4. MediaPipe Gesture-AprilTag height sorting
  • 5. MediaPipe Gesture-AprilTag distance sorting
  • 6. Block color sorting
  • 7. Block abnormal height sorting
  • 8. Block different shape sorting
  • 9. Block shape abnormal height sorting
  • 10. Yolov11 garbage sorting(Orin)
13. 3D Tracking and Grasping Course
  • 1. AprilTag Tracking and Grasping
  • 2. Color Block Tracking and Grasping
  • 3. KCF Object Tracking and Grasping
14. 3D Voice Control for Grasping Course (Sorting & Tracking)
  • 1. Voice Control AprilTag ID Sorting
  • 2. Voice Control Remove Height Abnormal AprilTag
  • 3. Voice Control Tracking and Grasping AprilTag
  • 4. Voice Control Color Block Sorting
  • 5. Voice Control Remove Height Abnormal Color Block
  • 6. Voice Control Tracking and Grasping Color Block
  • 7. Voice Control Yolov11 Garbage Recognition Sorting
  • 8. Voice Control Object Tracking and Grasping
15. AI 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
16. AI Model Developmentnew
  • 1.Register a model service provider account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. Deploy the RAG knowledge base
  • 6. AI Large Model Development - chatbot
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
17. AI Model – Text Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
18. AI Model – Voice Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
19. AI Model – Fun Applicationsnew
  • 1.AI Butler-Intent Inference
  • 2.AI Encyclopedia-Arithmetic Master
  • 3.AI Encyclopedia-You asked I Pointed
  • 4.AI Encyclopedia-Nutritional Analysis
  • 5.AI Encyclopedia-Problem Solving Expert
  • 6.AI Encyclopedia-English Spelling
  • 7.AI Butler-Garbage Sorting
  • 8.AI Top Chef
20. MoveIt Course
  • 1. MoveIt Configuration
  • 2. MoveIt Usage
  • 3. MoveIt Drive Real Robot
  • 4. MoveIt Random Movement
  • 5. MoveIt Forward Kinematics Design
  • 6. MoveIt Inverse Kinematics Design
  • 7. MoveIt Cartesian Path
  • 8. MoveIt Scene Design
  • 9. MoveIt Trajectory Planning
  • 10. MoveIt Collision Detection
21. YOLOv11 Development Course (ORIN)
  • 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 Conversion
  • 9. Dataset Annotation
  • 10. Model Training_Conversion
  • 11. Model Prediction
22. YOLOv11 Development Course (Jetson Nano)
  • 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 Conversion
  • 9. Model Prediction
23. 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
24. Basic Control Course
  • 1. Control RGB Light
  • 2. Control Buzzer
  • 3. Control Single Servo
  • 4. Read Servo Angle
  • 5. Control All Servos
  • 6. DOFBOT Up Down Left Right Movement
  • 7.DOFBOT dancing
  • 8. DOFBOT Memory Actions
  • 9. DOFBOT Grab Blocks
  • 10. Porter
  • 11. Stack Blocks
  • 12. Two DOFBOT Synchronized Actions
25. Jetson Board Basic Course
  • 1. Jetson board Introduction
  • 2. Write system image
  • 3. Re-write system image
  • 4. System startup
  • 5. System and desktop introduction
  • 6. System expansion
  • 7. Network Configuration
  • 8. SSH remote login and file transfer
  • 9. VNC Remote Login
  • 10. System backup
  • 11. Increased swap space
  • 12. Use of Jtop
26. GPIO Hardware Control Course
Jetson Nano
  • 1.GPIO library API usage
  • 2. Hardware library configuration
  • 3. Pin reading function
  • 4. Pin level output control
  • 5. Control LED
  • 6. External device serial communication
  • 7.I2C communication
Jetson Orin Nano_NX
  • 1.GPIO description
  • 2.GPIO library installation
  • 3.GPIO reading
  • 4.GPIO output
  • 5.Serial communication
  • 6.I2C communication
27. OpenCV 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. 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
28. Visual Basics Course
  • 1. Camera display
  • 2. Color Calibration
  • 3. Color recognition
  • 4. Face Detection
  • 5.1 Garbage Identification(Jetson-Nano)
  • 5.2 Garbage Identification(Orin)
  • 6.1 Object Recognition(Jetson-Nano)
  • 6.2 Object Recognition(Orin)
  • 7. Label code recognition
  • 8.PID algorithm basics
29. 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
30. Visual Advanced Course
  • 1.QR code
  • 2.Human pose estimation
  • 3.Target Detection
  • 4.AR vision
  • 5.ROS+Opencv basics
  • 6.AR QR code
  • 7.Face recognition
  • 8.harris corner detection
  • 9.Target tracking algorithm
  • 10.Contour moment
  • 11.Polygon outline
  • 12.Discrete fourier transform algorithm
  • 13.Edge detection algorithm
  • 14.Face detection algorithm
  • 15.Optical flow detection algorithm
  • 16.Contour detection
  • 17.General contour detection
  • 18.Feature point tracking
  • 19.HLS color filtering
  • 20.Hough circle detection
  • 21.Hough linear detection
  • 22.HSV color filtering
  • 23.LK optical flow algorithm
  • 24.detection algorithm
  • 25.Phase dependent displacement detection
  • 26.Image pyramid sampling algorithm
  • 27.RGB color filtering
  • 28.Clear background detection algorithm
  • 29.Simplified optical flow algorithm
  • 30.Simple filter
  • 31.Threshold image processing
  • 32.Watershed segmentation algorithm
31. Jupyter Lab Usage Course
  • 1. Read the document before operation
  • 2. Jupyter lab environment construction tutorial
  • 3. Basic tutorial of Jupyter lab
  • 4. Jupyter widgets configuration tutorial
  • 5. Getting Started Tutorial for JupyterWidgets Controls
Download
  • APP(Android/iOS)
  • SystemFile_OrinNANOSUPER
  • SystemFile_OrinNXSUPER
  • SystemFile_NANO4GB_SUB
  • VM_File
  • Code
  • Instruction_Manual
  • Hardware_Info
  • 3D_Model_File
  • ROS2_Basic_VM
  • Tools
Download ZIP

Welcome to DOFBOT Pro repository

1.GPIO library API usage

API usage of GPIO library

The Jetson GPIO library provides all the public APIs provided by the RPi.GPIO library. The usage of each API is discussed below:

1. Importing the library

To import the Jetson.GPIO module, use:

xxxxxxxxxx
import Jetson.GPIO as GPIO

In this way, you can refer to the module as GPIO in the rest of the application. The module can also be imported using the name RPi.GPIO instead of Jetson.GPIO is used for existing code that uses the RPi library.

2. Pin numbering

The Jetson GPIO library provides four ways to number IO pins. The first two correspond to the modes provided by the RPi.GPIO library, namely BOARD and BCM, which refer to the pin numbers of the 40-pin GPIO header connector and the Broadcom SoC GPIO numbers respectively. The remaining two modes, CVM and TEGRA_SOC, use strings instead of numbers, and the numbers correspond to the signal names on the CVM CVB connector and the Tegra SoC respectively.

To specify which mode you are using (mandatory), use the following function call:

xxxxxxxxxx
GPIO.setmode(GPIO.BOARD)# or
​
GPIO.setmode(GPIO.BCM)# or
​
GPIO.setmode(GPIO.CVM)# or
​
GPIO.setmode(GPIO.TEGRA_SOC)

To check which mode has been set, you can call:

xxxxxxxxxx
mode = GPIO.getmode()

The mode must be GPIO.BOARD, GPIO.BCM, GPIO.CVM, GPIO.TEGRA_SOC, or None.

3. Warning

The GPIO you are trying to use may already be in use outside of the current application. In this case, the Jetson GPIO library will warn you if the GPIO used is configured for anything other than the default direction (input). It will also warn you if you try to clean up before setting the mode and channel. To disable warnings, use:

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GPIO.setwarnings(False)

4. Setting up channels

GPIO channels must be set up before using them as input or output. To configure a channel as an input, call:

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GPIO.setup(channel, GPIO.IN)

To set a channel as an output, call:

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GPIO.setup(channel, GPIO.OUT)

You can also specify an initial value for an output channel:

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GPIO.setup(channel, GPIO.OUT, initial=GPIO.HIGH)

When setting a channel as an output, you can also set multiple channels at once:

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channels = [18, 12, 13]
​
GPIO.setup(channels, GPIO.OUT)

5. Input

To read the value of a channel, use:

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GPIO.input(channel)

This will return either GPIO.LOW or GPIO.HIGH.

6. Output

To set the value of a pin configured as an output, use:

xxxxxxxxxx
GPIO.output(channel, state)

State can be either GPIO.LOW or GPIO.HIGH.

You can also output to a list of channels or a tuple:

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channels = [18, 12, 13] # or use tuples
​
GPIO.output(channels, GPIO.HIGH) # or GPIO.LOW

set first channel to HIGH and rest to LOW

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GPIO.output(channel, (GPIO.LOW, GPIO.HIGH, GPIO.HIGH))

7. Cleanup

At the end of your program, it is a good idea to clean up the channels so that all pins are set to their default state. To clean up all used channels, use:

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GPIO.cleanup()

If you don't want to clean up all channels, you can also clean up a single channel or a list or tuple of channels:

xxxxxxxxxx
GPIO.cleanup(chan1) # cleanup only chan1
​
GPIO.cleanup([chan1, chan2]) # cleanup only chan1 and chan2
​
GPIO.cleanup((chan1, chan2)) # does the same operation as previous
statement

8. Jetson module information and library version

To get information about the Jetson module, use/read:

GPIO.JETSON_INFO

This gives a Python dictionary with the following keys: P1_REVISION, RAM, REVISION, TYPE, MANUFACTURER, and PROCESSOR. All values ​​in the dictionary are strings, except P1_REVISION which is an integer.

To get information about the library version, use/read:

GPIO.VERSION

This provides a string in the format of XYZ version.

9. Interrupts

In addition to busy polling, the library provides three methods of monitoring input events:

wait_for_edge() function

This function blocks the calling thread until a provided edge is detected. The function can be called as follows:

GPIO.wait_for_edge(channel, GPIO.RISING)

The second argument specifies the edge to detect, which can be GPIO.RISING, GPIO.FALLING, or GPIO.BOTH. You can optionally set a timeout if you only want to limit the wait to a specified amount of time:

timeout is in milliseconds

GPIO.wait_for_edge(channel, GPIO.RISING, timeout=500)

This function returns the channel on which the edge was detected, or None if a timeout occurred.

event_detected() function

This function can be used to periodically check if an event has occurred since the last call. The function can be set up and called as follows:

set rising edge detection on the channel

GPIO.add_event_detect(channel, GPIO.RISING)

run_other_code()

if GPIO.event_detected(channel):

do_something()

As before, you can detect events for GPIO.RISING, GPIO.FALLING, or GPIO.BOTH.

Run callback function when edge detected

This function can be used to run a second thread for the callback function. Thus, the callback function can run concurrently with the main program in response to an edge. This function can be used as follows:

define callback function

def callback_fn(channel):

print("Callback called from channel %s" % channel)

add rising edge detection

GPIO.add_event_detect(channel, GPIO.RISING, callback=callback_fn)

You can also add multiple callbacks if you want:

def callback_one(channel):

print("First Callback")

def callback_two(channel):

print("Second Callback")

GPIO.add_event_detect(channel, GPIO.RISING)

GPIO.add_event_callback(channel, callback_one)

GPIO.add_event_callback(channel, callback_two)

In this case, the two callbacks are run sequentially, not simultaneously, because there is only one thread running all the callback functions.

To prevent the callback function from being called multiple times by collapsing multiple events into one, a bounce time can be optionally set:

bouncetime set in milliseconds

GPIO.add_event_detect(channel, GPIO.RISING, callback=callback_fn,bouncetime=200)

If edge detection is no longer needed, it can be removed as follows:

GPIO.remove_event_detect(channel)

10. Checking the functionality of a GPIO channel

This function allows you to check the functionality of a provided GPIO channel:

GPIO.gpio_function(channel)

The function returns either GPIO.IN or GPIO.OUT.

11. PWM

See samples/simple_pwm.py for details on how to use PWM channels.

The Jetson.GPIO library only supports PWM on pins that come with a hardware PWM controller. Unlike the RPi.GPIO library, the Jetson.GPIO library does not implement software-emulated PWM. The Jetson Nano supports 2 PWM channels, while the Jetson AGX Xavier supports 3 PWM channels. The Jetson TX1 and TX2 do not support any PWM channels.

The system pin mux must be configured to connect the hardware PWM controller to the relevant pins. If the pinmux is not configured, the PWM signals will not reach the pins! The Jetson.GPIO library does not dynamically modify the pinmux configuration to achieve this. Read the L4T documentation for details on how to configure the pinmux

Full English instructions are available at: https://github.com/NVIDIA/jetson-gpio

Use of Jtop
Hardware library configuration

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