Waveshare JetRacer Professional Version ROS AI Kit A with NVIDIA Jetson Nano Developer Kit (B01)

This is a high-performance AI intelligent robot car specially made for students who learn and develop ROS systems. It adopts the ROS robot system and adopts dual controllers. The sub-controller adopts the Raspberry Pi RP2040 microcontroller, and the host controller adopts JETSON-NANO-DEV- KIT-A AI controller. The module integrates the OLED, servo motor drive circuit, battery protection circuit, an audio output circuit, etc., which saves you the trouble of soldering the circuit, supports unloading and charging and does not need to repeatedly remove the battery. Built-in IMU attitude sensor, DC encoder motor controls the speed through PID, and can output wheel odometer. The software is compatible with the NVIDIA Jetbot open-source project program, and supports AI deep learning, SLAM mapping and navigation, OpenCV visual processing, intelligent voice interaction, and other functions.

Features:

• Support 3 x 2600mah 18650 batteries (not included), up to 12.6V voltage output, stronger motor power.
• Onboard S-8254AA + AO4407A lithium battery protection circuit, with anti-overcharge, anti-over-discharge, anti-over-current and short-circuit protection functions.
• The onboard INA219 chip is for real-time monitoring of battery voltage and charging current.
• Onboard 0.91-inch 128×32 resolution OLED, real-time display of car IP address, memory, battery power, etc.
• The onboard MPU9250 IMU attitude sensor can accurately determine the direction of the car.
• Adopt coded DC gear motor, support PID debugging, and output wheel odometer.
• Adopt Raspberry Pi RP2040 dual-core microcontroller, with better real-time performance and higher control accuracy.
• Using ROS robot system development, a large number of visual software tools, and more ways to develop powerful functions.
• Support IIC slave mode control, no need to transplant, fully compatible with NVIDIA Jetbot open-source project program, easy to achieve AI functions such as obstacle avoidance and autonomous line inspection.
• Interface Introduction

• Wiki