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Grove Base Shield V2.0 for Arduino

SKU TPX00030 Barcode 103030000 Show more
Original price €4,54
Original price €4,54 - Original price €4,54
Original price €4,54
Current price €5,54
€5,54 - €5,54
Current price €5,54
VAT included

The easiest way for connecting your Arduino boards with Grove sensors!

Overview

Grove Base Shield provides a simple way to connect with Arduino boards and help you get rid of breadboard and jumper wires. With the 16 on-board Grove Connectors, you can easily connect with over 300 Grove modules! The pinout of Base Shield V2 is compatible with the Arduino UNO, Arduino Leonardo and the Arduino Mega.
The Arduino Shield usually has the same pin position as the Arduino development board and can be stacked and plugged into the Arduino to implement specific functions.

Power Compatible:

Every Grove connector has four wires, one of which is the VCC. However, not every micro-controller main board needs a supply voltage of 5V, some boards only need 3.3V. That's why we add a power toggle switch to Base Shield V2 so that you can select the suitable voltage of the micro-controller main board you are using via this switch.
For example, if you are using Arduino UNO with Base Shield V2, please turn the switch to 5V position; while using Seeeduino Arch with Base Shield V2, please turn the switch to 3.3V.

Board Compatible:

The pinout of Base Shield V2 is the same as Arduino Uno R3, however Arduino Uno is not the only one that the Base Shield V2 is compatible with, here we listed the boards that we have confirmed that can be used with Base Shield V2:

  • Arduino Uno
  • Seeeduino V4.2
  • Arduino Mega / Seeeduino Mega
  • Seeduino LoraWan
  • Arduino Leonardo / Seeeduino Lite
  • Arduino 101
  • Arduino Due
  • Intel Edison
  • Linkit One 

Conformities

The following Declarations of Conformities have been granted for this board:
CE
For any further information about our certifications please visit docs.arduino.cc/certifications

Get Inspired

PROJECT HUB
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Project Tutorial by fullmakeralchemist

An intelligent device to track moves with responses during an interactive space with mapping, backlight, music and smart sculptures. This project makes use of a machine learning algorithm capable of tracking and detecting moves to identify associated gesture recognition through a microcontroller. Smart sculptures, lighting, music and video projection to trigger with each assigned gesture, creating a powerful AV experience highlighting the incredible potential of TinyML for the performing arts. This allows the corresponding media set Tiny ML in interactive to play when the right move was made because all these elements interact to create a new experience. This allows us to create Interactive installations, these sculptures use a combination of motors, sensors, and other electronics to create an immersive and interactive experience for the viewer. They may include projections, sound, and other sensory elements to create a complete experience.

read more
BLOG
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
May 22, 2023

With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where

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