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Arduino MKR CAN Shield

SKU ASX00005 Barcode 7630049200302 Show more
Original price €0
Original price €41,21 - Original price €41,21
Original price
Current price €41,21
€41,21 - €41,21
Current price €41,21
VAT included

Need to connect a device to a CAN (Controller Area Network) for communication within an automobile and with other CAN devices? The Arduino MKR CAN shield will provide automotive CAN connectivity.

Overview

With this shield you can easily connect to a CAN (Controller Area Network) Bus. Discover new possibilities of interaction between your Arduino MKR Board and the CAN ecosystem.

The MKR CAN shield can simplify the connection of the MKR boards with industrial systems and especially with automotive applications. This shield opens a new set of possible applications like smart vehicles, autonomous cars and drones. A CAN connection also provides the possibility to connect a MKR board directly with several types of industrial grade sensors, motors and displays.

Notice: On some boards, the bottom silk is mirrored. Please refer to the top silk for guidance. If you need further assistance, contact our support team


Tech specs

Protocol CAN Bus
Interface SPI
Circuit Operating Voltage 3.3 V
Controller Microchip MCP2515 (datasheet)
Transceiver NXP TJA1049 (datasheet)
Buck converter Texas Instruments TPS54232 (datasheet)
Vin (screw connector) 7 V - 24 V
Vin (header) 5 V
Compatibilty MKR size
  Switchable onboard termination resistor
   

Conformities

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

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 Arduino MKR CAN Shield is open-source hardware! You can build your own board using the following files:

EAGLE FILES IN .ZIP SCHEMATICS IN .PDF
 

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.

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These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
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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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