AIoT SerBot G

1- C/C++, Python3.
2- Supporting all popular AI frameworks.
3- Lidar and sensors connectivity.

Categories: , ,

Description

Product Features

  • AI application practice equipment based on indoor service robot platform
  • Main processor is GPU supercomputer platform for edge device
  • 7-inch touch display with 1024×600 resolution and 8M pixel 160° wide-angle camera
  • Gigabit Ethernet, dual-band Wi-Fi (2.4GHz, 5GHz), and Bluetooth 4.2
  • Voice recognition and audio play through digital microphone and speaker
  • Supports various IoT sensor modules through four exclusive expansion interfaces
  • 3-axis omni wheel to maximize the movement efficiency and minimize the turning radius
  • Multiple PowerPath blocks allowing to practice even while the battery is charging
  • Robot standard middleware ROS2 and Pop library are provided
  • Supports CUDA-based PyTorch and Tensorflow artificial intelligence framework
  • Supports web browser-based Google block coding platform (Blockly)
  • Supports a public integrated development environment based on Visual Studio Code for professional application development
  • Service robot learning content based on artificial intelligence and deep learning is provided

Software Specifications

Training Contents

  1. Artificial Intelligence and Autonomous Driving
    • 1.1. Components of Autonomous Driving
    • 1.2. Autonomous Driving Overview
  2. Environment for Experiment
    • 2.1. AIoT SerBot-G
    • 2.2. Communication between PC and AIoT SerBot-G
    • 2.3. Development Environment
  3. Control AIoT SerBot-G
    • 3.1. 3WD Omni Wheel Moving Device
    • 3.2. 9DOF IMU Sensor
    • 3.3. Ultrasonic Sensor
    • 3.4. Psd Sensor
  4. CAN Protocol
    • 4.1. CAN Network
    • 4.2. CAN Communication
    • 4.3. CAN Communication in Linux
    • 4.4. Example of CAN Protocol Application
  5. Moving Device Library based on CAN Communication
    • 5.1. Start Library
    • 5.2. Moving Device Control Library based on CAN Communication
    • 5.3. Implement Sensor Action and Broadcast Reception
  6. MQTT
    • 6.1. MQTT Standard
    • 6.2. MQTT Broker and Client
    • 6.3. Topic
    • 6.4. Session
    • 6.5. MQTT Development Environment
    • 6.6. Remote Control of MQTT Moving Device
  7. LiDAR
    • 7.1. LiDAR Sensor
    • 7.2. LiDAR Control
    • 7.3. Avoidance Driving using LiDAR
  8. Artificial Intelligence
    • 8.1. Machine Learning and Perceptron
    • 8.2. Neural Network and Learning
    • 8.3. Machine Learning Framework
  9. Autonomous Driving
    • 9.1. Vision Processing
    • 9.2. Deep Learning and Convolutional Neural Network
    • 9.3. Lane Recognition based on Deep Learning
    • 9.4. Object Detection and Moving Object Control

Appendix

  • A. Flame Sensor
  • B. PIR Sensor
  • C. ECO Sensor
  • D. CO2
  • E. Dust Sensor
  • F. Thermopile Sensor
  • G. Micro Wave Sensor
  • H. Peripheral
  • I. Pixel Display

Layout

Components

 

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