SparkFun JetBot AI Kit Jetson Nano 人工智慧小車開發套件 V2.1 NVIDIA 推薦

描述

NVIDIA 推薦 SparkFun JetBot AI Kit Jetson Nano  V2.1 人工智慧小車開發套件 本套件不含 Jetson Nano 開發板

SparkFun JetBot AI Kit Jetson Nano 人工智慧小車開發套件 可立即組裝，且過程簡單又有趣，完全不需高超技術。將 SparkFun Qwiic 生態系統搭配 NVIDIA Jetson Nano 與 JetBot 人工智慧套件一起使用，在新原型中快速連結感應器、LCD 和馬達驅動器。此外，套件還隨附互動式人工智慧教學、JetBot ROS (機器人作業系統) 以及不需額外設定即可使用的 AWS 雲端支援。

使用 JETBOT 人工智慧機器人套件 啟動你的人工智慧, SparkFun JetBot AI Kit 是一個機器人平台，可由 NVIDIA 的Jetson Nano Developer Kit 提供人工智慧運算。這個SparkFun 套件基於開源NVIDIA JetBot！  這個版本的 SparkFun JetBot AI 套件是一個很棒的啟動板，本套件可以為已經擁有Jetson Nano的製造商，學生和愛好者創建全新的AI項目，並希望通過他們的機器學習平台邁出下一步。它易於設置和使用，並與許多流行的配件兼容。幾個教程向您展示如何利用 AI 的強大功能來訓練 SparkFun JetBot 跟踪對象，避免碰撞等等。Jetson Nano Developer Kit提供了有用的工具，如 Jetson GPIO Python 庫，兼容常見的傳感器和外設; 包括一些新的python與SparkFun的兼容性 Qwiic 快速連接系統。

該套件包括開始使用JetBot所需的一切，減去Jetson Nano開發套件，Phillips頭螺絲刀，基本焊接設置和Ubuntu桌面GUI。如果您需要這些，請查看包含標籤，了解我們目錄中的一些建議。請注意，只有使用完整的5V-4A電源才能並行運行多個神經網絡。

JetBot AI Kit Jetson Nano 人工智慧小車開發套件 的特色

• 適用於I ² C通訊的SparkFun Qwiic生態系統
• 可以使用GPIO接頭上的4個Qwiic連接器擴展生態系統
• 示例代碼：基本運動，遙距操作，避免碰撞和對象跟隨
• 緊湊的外形尺寸可優化NVIDIA現有的神經網絡
• 136°FOV攝像機可實現機器視覺
• 預閃microSD卡
• 機箱組裝提供可擴展的架構

本套件包含下列項目

• • 64GB MicroSD卡-預閃的SparkFun JetBot映像：
    • Nvidia Jetbot基本映像已安裝以下內容：SparkFun Qwiic python庫軟件包
    • Edimax WiFi適配器驅動程序
    • 綠草地
    • Jetbot ROS
  • 豹成像136FOV廣角相機和帶狀電纜
  • EDIMAX WiFi適配器
  • 亞博官網SparkFun Qwiic電機驅動器
  • 英國威廉希爾SparkFun Micro OLED Breakout（Qwiic）
  • 完成完整功能的機器人所需的所有硬件和原型電子產品！
  • 注意-組裝時需要使用十字螺絲刀，並且由於Qwiic焊接而不再需要！

  不包括的物品，但您需要：

  • NVIDIA Jetson Nano開發人員套件
  • USB鍵盤和鼠標
  • 電腦顯示器（HDMI或DP）和連接器電纜

Sparkfun 線上組裝教學

教學文件與技術文件

• Getting Started With Jetson Nano Developer Kit
• Assembly Guide for SparkFun JetBot AI Kit V2.0
• SparkFun Hookup Guide for Qwiic Motor Driver
• SparkFun Hookup Guide for Qwiic Micro OLED (Qwiic)
• SparkFun Hookup Guide for Qwiic pHAT v2.0
• JetBot ROS GitHub Repository
• Getting Started with AWS IoT Greengrass
• Deploying Robotic Applications Using Machine Learning with Nvidia JetBot and AWS RoboMaker
• NVIDIA Jetson Forums
• NVIDIA JetBot GitHub Issues
• NVIDIA JetBot GitHub
• NVIDIA JetBot GitHub Wiki
• NVIDIA JetBot Software Setup
• SparkFun JetBot AI Kit v2.1 範例說明

SparkFun JetBot AI Kit v2.1 (Without Jetson Nano)

The SparkFun JetBot AI Kit is a robot platform that can be powered by your own Jetson Nano Developer Kit by NVIDIA. This SparkFun kit is based on the open-source NVIDIA JetBot! We understand that not everyone has access to multiple 3D printers on each floor, and a whole warehouse of electronics so we wanted to build a kit from ready to assemble parts to get you up and running as quickly as possible. This version of the SparkFun JetBot AI Kit is a great launchpad for creating entirely new AI projects for makers, students and enthusiasts who already own a Jetson Nano and are looking to take the next step with their machine learning platform. It’s straightforward to set up and use and is compatible with many popular accessories. Several interactive tutorials show you how to harness the power of AI to teach the SparkFun JetBot to follow objects, avoid collisions and more.

Additionally, the included image is delivered with the advanced functionality of JetBot ROS (Robot Operating System) and AWS RoboMaker Ready with AWS IoT Greengrass already installed. SparkFun’s JetBot AI Kit is the only kit currently on the market ready to move beyond the standard JetBot examples and into the world of connected and intelligent robotics.

The Jetson Nano Developer Kit offers useful tools like the Jetson GPIO Python library, and is compatible with common sensors and peripherals; including some new python compatibility with the SparkFun Qwiic ecosystem.

This kit includes everything you need to get started with JetBot minus a Phillips head screwdriver and an Ubuntu desktop GUI. If you need these, check out the includes tabs for some suggestions from our catalog. Please be aware that the ability to run multiple neural networks in parallel may only be possible with a full 5V-4A power supply.

• SparkFun Qwiic ecosystem for I²C communication
• Ecosystem can be expanded using 4x Qwiic connectors on GPIO header
• Example Code for: Basic Motion, Teleoperation, Collision avoidance, & Object Following
• Compact form factor to optimize existing neural net from NVIDIA
• 145° FOV camera for machine vision
• Pre-flashed microSD card
• Chassis assembly offers expandable architecture