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Artemis ATP Automatic Faucet

Voice controlled faucet that responds on the edge or by bluetooth executed on the SparkFun Artemis ATP Development board.

SparkFun Artemis ATP Development

Contents

Required Hardware

To run the examples, the following hardware is required:

Software Setup

The example in this repository is for use and execution within the Arduino development environment. This section details the steps required to setup Arduino for the examples.

Install Arduino

Arduino is avilable for a variety of platforms. To ensure compatiblity with the demos in this repository, the latest version should be installed.

The Arduino application is available for a variety of platforms and is available online from Arduino. Download the application from the Arduino website using this link.

Install the TensorFlowLite Library

The examples utilize the TensorFlowLite Arduino libary, which is installed using the Arduino Library Manager.

To install this library, use the following steps:

  • In Arduino, select the "Manage Libraries..." menu item. Tools > Manage Libraries...
  • In the Library Manager interface, search for tensorflow
  • Select the library Arduino_TensorFlowLite by TensorFlow Authors
  • Select the non-precompiled version of the libary
  • Select the Install button to install the library

Install the SparkFun Boards Package

Load the SparkFun Boards package into the Arduino Board Manger.

To install package, use the following steps:

  • In Arduino, open the Preferences menu item. File > Preferences, (macOS) Arduino > Preferences

  • Add the following path to the Additional Boards Manager URLs: path in preferences.

    https://raw.githubusercontent.com/sparkfun/Arduino_Boards/master/IDE_Board_Manager/package_sparkfun_index.json

  • Select the OK button to save the preferences.

Install the SparkFun Artemis Boards Package

Once the location of the SparkFun boards package is set in the preferences, the board definition package for the SparkFun Apollo3 boards must be installed.

To install package, use the following steps:

  • In Arduino, open the Preferences menu item. Tools > Board "..." > Manage Boards...
  • Search for SparkFun
  • Select the **SparkFun Apollo3 Boards" package
  • Close the dialog
  • Select the SparkFun Edge Board. Tools > Boards "..." > SparkFun RedBoard Artemis ATP
  • Set the Bootloader to the SparkFun Variable Loader. ***Tools > Bootloader "..." > SparkFun Variable Loader..."

Running an Example

Configure the Hardware

  • Plug USB cable into computer
  • Plug in Artemis ATP board to adpater

Configure the Software

  • Install and setup Arduino as described above.
  • Download (or clone) this repo to favorite location
  • Open Arduino
  • Open an example from this repo through Arduino by clicking on the .ino file in the example directory
    • micro_speech
  • Select the SparkFun RedBoard Artemis ATP board (Tools->Board under 'SparkFun Apollo3')
  • Select the proper port to connect with the baord (Tools->Port)
    • Windows - COM Port
    • Linux - /dev/ttyUSB*, where * is a number. NOTE: Permissions on this device needs to include user read and write (chmod 666)
    • macOS - /dev/cu.usbserial*
  • Change the Bootloader from Ambiq Secure Bootloader (Default) to SparkFun Variable Loader (Enable w/ Artemis Bootloader) (Tools->Bootloader)
    • (Your boards have received the upgraded bootloader already)
  • Change the SVL Baud Rate from 921600 to 230400

Upload

  • Compile with the Verify button (checkmark symbol)
  • Upload with the Upload button (arrow symbol)
    • If uploading fails try lowering the bootloader baud rate
  • Note: You will see some warnings from the compiler, these can be ignored.
  • Note: The bootloader attempts to upload for 3 times. If all three attempts fail, try adjusting the baud rate.

Serial Monitor

  • Open the Serial Monitor by clicking on the magnifying glass icon (top right) or (Tools->Serial Monitor)

Change Voice Commands

This repository has a model that is trained to recognize "Up" and "Down". An example of how to train a new model based on Google collected sample phrases is included in the tensorflow micro_speech example repository. The method listed utilizes Google Colaboratory to run the training session - an Junypter notebooks based system that presents a Python based notebook and abstracts the management of compute resources.