Overview
The MKR Proto Large Shield fits onto your MKR board using its provided female/male headers and enables you to fix it wherever you want with the mounting holes. This shield features a larger prototyping area with more than 300 solder points and makes connecting components to your board super simple, thanks to the silk that indicates the MKR board pins location
Tech specs
| Digital I/O Pins | 21 |
| PWM Digital I/O Pins | depending on the board |
| Analog Input Pins | 7 |
| Analog Output Pins | depending on the board |
| DC Current per I/O Pin | depending on the board |
| DC Current for 3.3V Pin | depending on the board |
| DC Current for 5V Pin | depending on the board |
| Lenght | 80 mm |
| Width | 50 mm |
| Weight | 19 gr |
Conformities
Resources for Safety and Products
Manufacturer Information
The production information includes the address and related details of the product manufacturer.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/
Responsible Person in the EU
An EU-based economic operator who ensures the product's compliance with the required regulations.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc
Documentation
OSH: Schematics
The MKR Large Proto Shield is open-source hardware! You can build your own board using the following files:
EAGLE FILES IN .ZIP SCHEMATICS IN .PDFGet Inspired
Combine the Alvik, Modulinos, Nano ESP32 and the Nicla Vision to create a robot that can respond to your greeting!
Shortly after attending a recent tinyML workshop in Sao Paolo, Brazil, Joao Vitor Freitas da Costa was looking for a way to incorporate some of the technologies and techniques he learned into a useful project. Given that he lives in an area which experiences elevated levels of pickpocketing and automotive theft, he turned his attention to a smart car security system. His solution to a potential break-in or theft of keys revolves around the incorporation of an Arduino Nicla Vision board running a facial recognition model that only allows the vehicle to start if the owner is sitting in the driver’s seat. The beginning of the image detection/processing loop involves grabbing the next image from the board’s camera and sending it to a classification model where it receives one of three labels: none, unknown, or Joao, the driver. Once the driver has been detected for 10 consecutive seconds, the Nicla Vision activates a relay in order to complete the car’s 12V battery circuit, at which point the vehicle can be started normally with the ignition. Through this project, da Costa was able to explore a practical application of vision models at-the-edge to make his friend’s car safer to use. To see how it works in more detail, you can check out the video below and delve into the tinyML workshop he attended here.
