CAN-BUS Shield v2
Sold outWith its long travel distance, great communication speed and high reliability, CAN-BUS is one of the most common industrial bus.
Overview
It is commonly found on modern machine tools and as an automotive diagnostic bus.
Thanks to the CAN-BUS, makers are able to hack their cars!
It adopts MCP2515 CAN-BUS controller with SPI interface and MCP2551 CAN transceiver to give you Arduino CAN-BUS capability. Default pinout is OBD-II and CAN standard pinout can be selected by switching jumpers on DB9 interface.
Moreover, it has the TF card slot for data storage and the CS pin that can be set to D4 or D5.
The INT pin can also be set to D2 or D3 by switching jumpers on the back of the shield.
CAN-BUS Shield Works perfectly with Arduino UNO (ATmega328), Arduino Mega (ATmega1280/2560) as well as Arduino Leonardo (ATmega32U4).
Features:
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Implements CAN V2.0B at up to 1 Mb/s
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Industrial standard 9 pin sub-D connector
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OBD-II and CAN standard pinout selectable.
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Changeable chip select pin
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Changeable CS pin for TF card slot
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Changeable INT pin
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Screw terminal that easily to connect CAN_H and CAN_L
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Arduino Uno pin headers
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2 Grove connectors (I2C and UART
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SPI Interface up to 10 MHz
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Standard (11 bit) and extended (29 bit) data and remote frames
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Two receive buffers with prioritized message storage
Conformities
Get Inspired
A quick tutorial on how to interface the voice recognition module with few examples.
For people not familiar with American Sign Language (ASL), being able to recognize what certain hand motions and positions mean is a nearly impossible task. To make this process easier, Hackster.io user ayooluwa98 came up with the idea to integrate various motion, resistive, and touch sensors into a single glove that could convert these signals into understandable text and speech. The system is based around a single Arduino Nano board, which is responsible for taking in sensor data and outputting the phrase that best matches the inputs. The orientation of the hand is ascertained by reading values from the X, Y, and Z axes of a single accelerometer and applying a small change based upon prior calibration. Meanwhile, resistive flex sensors spanning the length of each finger produce a different voltage level according to the bend’s extent. At each iteration of the program’s main loop, a series of Boolean statements are evaluated to pick the phrase that best matches the current finger bends and hand orientation, and this data is then outputted via the UART pins to an attached Bluetooth® HC-05 module. The final component is a connected phone running a custom app that takes the incoming words from Bluetooth® and saves them for text-to-speech output when the button is pressed. To see more about this project, you can read ayooluwa98’s write-up here on Hackster.io.
