This is Part 3 of a 3-part series covering a real-time voice-to-sign-language translation system. In Part 1, I covered the React frontend that captures speech, processes it with Amazon Nova 2 Sonic, and publishes cleaned sentence text via MQTT. In Part 2, I covered the AWS CDK stack that routes IoT Core messages through Lambda to AppSync for real-time GraphQL subscriptions.

This post covers the final piece — the edge AI agent that actually makes the physical hand move. It is a Strands Agent running on an NVIDIA Jetson that subscribes to MQTT commands from the frontend, uses Amazon Nova 2 Lite to invoke the fingerspell tool, drives the Pollen Robotics Amazing Hand's Feetech SCS0009 servos for ASL fingerspelling letter by letter, records video of the hand in action, uploads it to S3, and publishes hand state back to IoT Core — which Part 2's infrastructure routes through to the frontend via AppSync.

The three repositories in the series:

1. Part 1 - Frontend and Voice Processing (amplify-react-nova-sonic-voice-chat-amazing-hand) — React web app that captures speech, streams to Nova 2 Sonic, publishes cleaned sentence text via MQTT
2. Part 2 - Cloud Infrastructure (cdk-iot-amazing-hand-streaming) — AWS CDK stack that routes IoT Core messages through Lambda to AppSync
3. This post (Part 3) - Edge AI Agent (strands-agents-amazing-hands) — Strands Agent powered by Amazon Nova 2 Lite on NVIDIA Jetson that translates sentence text to ASL servo commands, drives the Amazing Hand, and publishes state back

Goals

• Receive MQTT commands from the React frontend (plain text or JSON with sentence field) and drive the Amazing Hand servos for ASL fingerspelling
• Use the Strands Agents framework with Amazon Nova 2 Lite (us.amazon.nova-2-lite-v1:0) to invoke the fingerspell tool — the LLM passes the incoming text verbatim to the tool for letter-by-letter ASL spelling
• Fingerspell text using the 26-letter ASL alphabet (A-Z), with each letter held for 0.8 seconds and spaces adding a 0.4-second pause
• Control 8 Feetech SCS0009 servos (4 fingers x 2 joints) on the Pollen Robotics Amazing Hand via serial bus at 1M baud using the rustypot library
• Record video of the hand via OpenCV during each fingerspelling sequence, encode to H.264 MP4 via imageio-ffmpeg, upload to S3, and include a presigned URL in the state message
• Publish real-time hand state (servo angles, letter, video URL) to IoT Core over MQTT — which Part 2's CDK stack routes to AppSync for the frontend to consume
• Authenticate to AWS IoT Core using mTLS with X.509 device certificates
• Create a fresh agent instance per MQTT message to prevent conversation history accumulation and unbounded token growth
• Handle graceful shutdown with servo torque disable on SIGINT/SIGTERM

The Overall System

This diagram shows the complete end-to-end system. Part 3 is the edge device highlighted on the right — the NVIDIA Jetson running the Strands Agent that controls the Amazing Hand.

How Part 3 fits in:

• Part 1 (Frontend) publishes cleaned sentence text to the-project/robotic-hand/{deviceName}/action via MQTT
• Part 3 (This agent) subscribes to the /action topic, processes the command through the Strands Agent, drives the servos, records video, and publishes state back to /state
• Part 2 (Infrastructure) picks up the /state messages and routes them through Lambda to AppSync, where the frontend receives them via GraphQL subscriptions

Architecture

The agent is a Python application built on the Strands Agents framework. It runs as a long-lived MQTT listener on the NVIDIA Jetson, creating a fresh agent instance for each incoming message to keep memory bounded.

Agent Architecture​

Components:

• MQTT Listener (agent.py) — Subscribes to the action topic, parses incoming messages (plain text or JSON), and submits each action to a single-threaded agent executor to keep the AWS CRT MQTT event loop free
• Strands Agent — A fresh Agent instance created per message with Amazon Nova 2 Lite as the model, the fingerspell tool as the available action, and a MaxToolCallsHook (limit 3) to prevent runaway tool-call loops
• Fingerspell Tool (hand_control.py) — A @tool decorated function that the LLM invokes to spell text letter-by-letter using the 26-letter ASL alphabet
• Servo Controller — Uses rustypot.Scs0009PyController to communicate with 8 Feetech SCS0009 servos over serial at 1M baud. Each finger has two servos controlled by dedicated move functions (Move_Index, Move_Middle, Move_Ring, Move_Thumb)
• Video Recorder (video_recorder.py) — Background daemon thread captures frames via OpenCV, encodes to H.264 MP4 via imageio-ffmpeg, uploads to S3, and returns a presigned URL (1-hour expiry)
• State Publisher — Non-blocking MQTT publisher on a separate thread that sends hand state (finger angles, letter, video URL) to the /state topic with QoS 1

Data Flow​

Interactive Sequence Diagram​

How it works

MQTT Command Reception​

The agent subscribes to an MQTT action topic (e.g. the-project/robotic-hand/XIAOAmazingHandRight/action) using mTLS authentication with X.509 device certificates. The first connection uses clean_session=True to flush any stale session state, then reconnects with clean_session=False for normal operation.

When a message arrives, the handler tries to parse it as JSON and extract the sentence field. If JSON parsing fails, it treats the entire payload as plain text. The action is then submitted to a single-threaded executor (agent_executor) to keep the AWS CRT MQTT event loop free:

def on_message(topic, payload, dup, qos, retain, **kwargs):
    payload_str = payload.decode("utf-8")
    try:
        data = json.loads(payload_str)
        action = data.get("sentence", payload_str)
    except json.JSONDecodeError:
        action = payload_str
    agent_executor.submit(_process_action, action)

Strands Agent and Amazon Nova 2 Lite​

The Strands Agents framework provides the core AI reasoning loop. A fresh agent instance is created for every MQTT message — this is deliberate to prevent conversation history from accumulating across messages, which would cause unbounded token growth over time.

The agent uses Amazon Nova 2 Lite (us.amazon.nova-2-lite-v1:0) via the Bedrock Converse API. Nova 2 Lite was chosen for its low-latency tool-use responses, which is critical for real-time servo control. The agent is configured with a MaxToolCallsHook that cancels tool calls beyond 3 to prevent infinite LLM tool-call loops.

The agent runs in fingerspell-only mode — only the fingerspell tool is available. The system prompt instructs the LLM to pass the entire message verbatim to the fingerspell tool without shortening or modifying it. State messages include a letter field identifying the current ASL letter being signed.

Servo Hardware and Control​

The Amazing Hand — an open-source robotic hand designed by Pollen Robotics and manufactured by Seeed Studio — has 4 fingers (index, middle, ring, thumb — no pinky) with 2 Feetech SCS0009 servos per finger (8 servos total) connected via a Waveshare driver board over serial USB at 1,000,000 baud.

Each servo has an angle range of -90 to +90 degrees. Per-servo calibration offsets (MiddlePos) are applied during move operations to account for physical alignment:

MiddlePos = [-17, 8, -16, -4, -12, 10, -9, 9]

The control sequence for each finger:

1. Set goal speed for both servos (write_goal_speed) with a 0.2ms sleep between each speed write for serial bus timing
2. Convert angle to radians with calibration offset: np.deg2rad(MiddlePos[i] + angle)
3. Set goal position for both servos (write_goal_position)
4. 5ms sleep after positions are set before the next finger's commands

ASL Fingerspelling Tool​

The fingerspell(text) tool is decorated with @tool from the Strands framework, making it callable by the LLM during inference. It spells text letter-by-letter using the ASL alphabet. Each of the 26 letters (A-Z) is mapped to servo angle tuples for all 4 fingers. Each letter is held for 0.8 seconds, spaces add a 0.4-second pause, and non-letter characters are skipped. A state message with the current letter field is published after each letter.

Since the Amazing Hand has no pinky finger, ASL letters that require a pinky use the ring finger instead.

Video Recording Pipeline​

Video is recorded concurrently with each fingerspelling sequence:

1. Start recording — Before the agent is invoked, start_recording() launches a background daemon thread (video-capture) that captures frames from OpenCV VideoCapture(0) at the camera's native FPS (typically 30)
2. Stop and encode — After the agent completes, stop_recording_and_upload() stops the capture thread, converts frames from BGR (OpenCV) to RGB, and encodes to H.264 MP4 using imageio.v3 with the libx264 codec. The temp file is named hand_YYYYMMDD_HHMMSS_
3. Upload to S3 — The MP4 is uploaded to the configured S3 bucket (default: cc-amazing-video) with key videos/hand_YYYYMMDD_HHMMSS.mp4
4. Presigned URL — A presigned URL is generated with 1-hour expiry and appended to the last state message, which is re-published to the /state topic

State Publishing​

After each servo movement, the tool publishes a state message to the MQTT /state topic (e.g. the-project/robotic-hand/XIAOAmazingHandRight/state) with QoS 1. Publishing is non-blocking — it submits to a dedicated _publish_executor thread to avoid blocking the servo tool.

The state payload:

{
  "gesture": "fingerspell",
  "letter": "E",
  "ts": 1770550850,
  "fingers": {
    "index":  { "angle_1": 45, "angle_2": -45 },
    "middle": { "angle_1": 45, "angle_2": -45 },
    "ring":   { "angle_1": 45, "angle_2": -45 },
    "thumb":  { "angle_1": 60, "angle_2": -60 }
  },
  "video_url": "https://cc-amazing-video.s3.amazonaws.com/videos/hand_20260228.mp4?..."
}

The last published state is cached so that publish_state_with_video_url() can re-publish it with the presigned URL appended after video upload completes — without needing to re-read servo angles.

This state payload is what Part 2's CDK stack picks up via the IoT Rule, flattens in Lambda, and pushes into AppSync for the frontend to consume.

Threading Model​

The agent uses two thread pools and a daemon thread to keep operations non-blocking:

Graceful Shutdown​

On SIGINT or SIGTERM, the agent:

1. Sets a stop event to exit the main loop
2. Disables servo torque (write_torque_enable(1, 2)) to release the servos and prevent power draw
3. Disconnects from MQTT
4. Logs completion

Technical Challenges & Solutions

Challenge 1: Conversation History Bloat​

Problem: Strands Agents maintain conversation history by default. Over time, as hundreds of MQTT messages are processed, the token count grows unboundedly, increasing latency and cost.

Solution: A fresh Agent instance is created for every MQTT message. This discards all prior conversation history, keeping each invocation lightweight. Token usage (input, output, total) is logged after each invocation for monitoring.

Challenge 2: Runaway Tool-Call Loops​

Problem: The LLM might enter a loop of calling tools repeatedly — for example, calling fingerspell then deciding to call it again with modified text, then again.

Solution: A custom MaxToolCallsHook implementing the Strands HookProvider interface. It counts tool calls per agent invocation and cancels any tool call beyond the limit of 3. This is injected into the agent via hooks=[MaxToolCallsHook()].

Challenge 3: No Pinky Finger on the Amazing Hand​

Problem: The Pollen Robotics Amazing Hand has only 4 fingers (index, middle, ring, thumb) — no pinky. Several ASL letters require specific pinky positions (e.g. I, J, Y).

Solution: ASL letters that require a pinky use the ring finger instead. The 26-letter ASL alphabet is manually mapped to 4-finger servo angle tuples, approximating the correct hand shape with the available fingers.

Challenge 4: Serial Bus Timing​

Problem: Sending servo commands too quickly over the serial bus causes missed commands or erratic movement. The Feetech SCS0009 protocol requires time between operations.

Solution: A 0.2ms sleep is inserted between speed writes, and a 5ms sleep is added after both goal positions are set, giving the serial bus time to process each command before the next finger's sequence begins.

Getting Started

GitHub Repository: https://github.com/chiwaichan/strands-agents-amazing-hands

Prerequisites​

• NVIDIA Jetson (AGX Thor or Orin Nano Super) with Python 3.10+
• Pollen Robotics Amazing Hand connected via USB serial (Waveshare driver board)
• AWS IoT Core device certificates (certificate, private key, root CA)
• Amazon Bedrock access enabled for Nova 2 Lite in us-east-1
• USB camera connected to the Jetson
• S3 bucket for video storage (default: cc-amazing-video)

Installation​

git clone https://github.com/chiwaichan/strands-agents-amazing-hands.git
cd strands-agents-amazing-hands
pip install -e .

Running the Agent​

amazing-hand-agent \
  --endpoint your-iot-endpoint.iot.us-east-1.amazonaws.com \
  --cert certs/device.pem.crt \
  --key certs/device.pem.key \
  --ca certs/AmazonRootCA1.pem \
  --topic the-project/robotic-hand/XIAOAmazingHandRight/action \
  --serial-port /dev/amazing-hand-right \
  --s3-bucket cc-amazing-video

The agent will connect to IoT Core, subscribe to the action topic, and wait for commands. When a message arrives, it will process it through the Strands Agent, drive the servos, record video, and publish state back.

Summary

This post covered the edge AI agent — the final piece of the voice-to-sign-language translation system:

• Strands Agents framework with Amazon Nova 2 Lite for tool-use — a fresh agent per MQTT message prevents history bloat, with MaxToolCallsHook limiting calls to 3
• ASL fingerspelling with the 26-letter alphabet (A-Z), each letter held for 0.8 seconds — the fingerspell tool is decorated with @tool for LLM invocation
• 8 Feetech SCS0009 servos on 4 fingers controlled via rustypot over serial at 1M baud, with per-servo calibration offsets
• Video pipeline captures via OpenCV in a background daemon thread, encodes to H.264 MP4 via imageio-ffmpeg, uploads to S3, and includes a 1-hour presigned URL in the final state message
• Non-blocking threading with 2 thread pools (agent executor off MQTT event loop, state publisher) and a daemon thread for video capture
• Real-time state publishing to IoT Core after every servo movement — which Part 2's CDK stack routes through Lambda to AppSync, completing the feedback loop to the React frontend in Part 1
• Graceful shutdown disables servo torque on SIGINT/SIGTERM to release the servos and prevent power draw

I want to use the Jetson Nano to leverage any sensor readings captured by the ESP32C6 and use it for inferences downstream. In the past I would have tried to send the messages between the devices via AWS IoT Core, but over the wires using UART it is definitely much faster - single digit milliseconds over UART.

Here is the source code to use as a building block to enable a Seeed Studio XIAO ESP32C6 to send messages to a NVIDIA Jetson Nano Super over the UART protocol; uni-direction. The XIAO code is a PlatformIO project and the Jetson Nano Super is a Python script.

Source Code​

You can find the source code for this project here: https://github.com/chiwaichan/nvidia-jetson-nano-super-seeed-studio-xiao-esp32c6-uart

Recently I switched my Cat Feeder project's IaC to AWS CDK in favour of increasing my focus and productivity on building and iterating, rather than constantly mucking around with infrastructure everytime I resume my project after a break; which is rare and far between these days.

Just as with coding IoT microcontrollers such as the ESP32s, I want to get straight back into building every opportunity I get; so I am also switching away from Arduino based microcontroller development written in C++ - I don't have a background in C++ and to be honest this is the aspect I struggled with the most because I tend to forget things after not touching it for 6 months or so.

So I am switching to MicroPython to develop the logic for all my IoT devices going forward, this means I get to use Python - a programming lanaguge I work with frequently so there is less chance of me being forgetful when I use it at least once a month. MicroPython is a lean and efficient implementation of the Python 3 programming language that includes a subset of the Python standard library and is optimized to run on microcontrollers and in constrained environments - a good fit for IoT devices such as the ESP32!

What about all the Arduino hardware and components I already invested in?

Good news is MircoPython is supported on all ESP32 devices - based on the ones I myself have purchased; all I need to do to each ESP32 device is to flash it with a firmware - if you are impatient, you can scroll down and skip to below to the flashing the firmware section. When I first started Arduino, MicroPython was available to use, but that was 2 years ago and there were not as many good blog and tutorial content out there as there is today; I couldn't at the time work out how to control components such as sensors, servos and motors as well as I could with C++ based coding using Arduino; nowdays there are way more content to learn off and I've learnt (by PoCing individual components) enough to switch to MicroPython. As far as I understand it, any components you have for Arduino can be used in MicroPython, provided that there is a library out there that supports it, if there isn't then you can always write your own!

What's covered in this blog?

By the end of this blog, you will be able to send and receive MQTT messages from AWS IoT core using MicroPython, I will also cover the steps involved in flashing a MicroPython firmware image onto an ESP32C3. Although this blog has a focus and example on using an ESP32, this example can be applied to any micro-controllers of any brand or flavours, provided the micro-controller you are using supports MicroPython.

Flashing the MicroPython firmware onto a Seeed Studio XIAO ESP32C3

The following instructions works for any generic ESP32C3 devices!

Download the latest firmware from micropython.org​

https://micropython.org/download/ESP32_GENERIC_C3/

Next, I connected my ESP32C3 to my Mac and ran the following command to find the name of the device port

 /dev/ttyUSB0

My ESP32C3 is named "/dev/tty.usbmodem142401", the name for your ESP32C3 may be different.

Next, install esptool onto your computer, then run the following commands to flash the MicroPython firmware onto the ESP32C3 using the bin file you've just downloaded.

esptool.py --chip esp32c3 --port /dev/tty.usbmodem142401 erase_flash

esptool.py --chip esp32c3 --port /dev/tty.usbmodem142401 --baud 460800 write_flash -z 0x0 ESP32_GENERIC_C3-20240105-v1.22.1.bin

It should look something like this when you run the commands.

Install Thonny and run it. Then go to Tools -> Options, to configure the ESP32C3 device in Thonny to match the settings shown in the screenshot below.

If everything went well, you should see these 2 sections in Thonny: "MicroPython Device" and "Shell", if not then try clicking on the Stop button in the top menu.

AWS IoT Core Certificates and Keys​

In order to send MQTT messages to an AWS IoT Core Topic, or to receive a message from a Topic in reverse, you will need a set of Certificate and Key\s for your micro-controller; as well as the AWS IoT Endpoint specific to your AWS Account and Region.

It's great if you have those with you so you can skip to the next section, if not, do not worry I've got you covered. In a past blog I have a reference architecture accompanied by a GitHub repository on how to deploy resources for an AWS IoT Core solution using AWS CDK, follow that blog to the end and you will have a set of Certificate and Key to use for this MicroPython example; the CDK Stack will deploy all the neccessary resources and policies in AWS IoT Core to enable you to both send and receive MQTT messages to two separate IoT Topics.

Reference AWS IoT Core Architecture: https://chiwaichan.co.nz/blog/2024/02/02/feedmyfurbabies-i-am-switching-to-aws-cdk/

Upload the MicroPython Code to your device​

Now lets upload the MicroPython code to your micro-controller and prepare the IoT Certificate and Key so we can use it to authenticate the micro-controller to enable it to send and receive MQTT messages between your micro-controller and IoT Core.

Clone my GitHub repository that contains the MicroPython example code to publish and receive MQTT message with AWS IoT Core: https://github.com/chiwaichan/feedmyfurbabies-micropython-iot

It should look something like this.

Copy your Certificate and Key into the respective files shown in the above screenshot; otherwise, if you are using the Certificate and Key from my reference architecture, then you should use the 2 Systems Manager Parameter Store values create by the CDK Stack.

Next we convert the Certificate and Key to DER format - converting the files to DER format turns it into a binary format and makes the files more compact, especially neccessary when we run use it on small devices like the ESP32s.

In a terminal go to the certs directory and run the following commands to convert the certificate.pem and private.key files into DER format.

openssl rsa -in private.key -out key.der -outform DER
openssl x509 -in certificate.pem -out cert.der -outform DER

You should see two new files with the DER extension appear in the directory if all goes well; if not, you probably need to install openssl.

In Thonny, in the Files explorer, navigate to the GitHub repository's Root directory and open the main.py file. Fill in the values for the variables shown in the screenshot below to match your environment, if you are using my AWS CDK IoT referenece architecture then you are only required to fill in the WIFI details and the AWS IoT Endpoint specific to your AWS Account and Region.

Select both the certs folder and main.py in the Files explorer, then right click and select "Upload to /" to upload the code to your micro-controller; the files will appear in the "MicroPython Device" file explorer.

This is the moment we've been waiting for, lets run the main.py Python script by clicking on the Play Icon in green.

If all goes well you should see some output in the Shell section of Thonny.

The code in the main.py file has a piece of code that is generating a random number for the food_capacity percentage property in the MQTT message; you can customise the message to fit your use case.

But lets verify it is actually received by AWS IoT Core.

Alright, lets go the other way and see if we can receive MQTT messages from AWS IoT Core using the other Topic called "cat-feeder/action" we subscribed to in the MicroPython code.

Lets go back the AWS Console and use the MQTT test client to publish a message.

In the Thonny Shell we can see the message "Hello from AWS IoT console" sent from the AWS IoT Core side and it being received by the micro-controller.

This is the Part 4 and final blog of the series where I detail my journey in learning to build an IoT solution.

Please have a read of my previous blogs to get the full context leading up to this point before continuing.

• Part 1: I talked about setting up a Seeed AWS IoT Button
• Part 2: I talked about publishing events to an Adruino Micro-controller from AWS
• Part 3: I talked about my experience of using a 3D Printer for the first time to print a Cat Feeder

Why am I building this Feeder?​

I've always wanted to dip my toes into building IoT solutions beyond doing what a typical tutorial teaches in only turning on LEDs - I wanted to build something that would used everyday. Plus, I often forget to feed the cats while I am away from home (for the day), so it would be nice to come home to a non-grumpy cat by feeding them remotely any time and from any where in the world using the internet.

What was used to build this Feeder?​

• A 3D Printer using PLA as the filament material.
• An Arduino based micro-controller - in this case a Seeed Studio XIAO ESP32C3
• A couple of motors and controllers
• AWS Services
• Seeed AWS IoT Button
• Some code
• and some cat food

So how does it work and how is it put together?​

To simply describe what is built, the Feeder uses an Iot button click to trigger events over the internet to instruct the feeder to dispense food into one or both food bowls.

Here are some diagrams describing the architecture of the solution - the technical things that happens in-between the IoT button and the Cat Feeder.

When the Feeder receives a MQTT message from the AWS IoT Core Service, it runs the motor for 10 seconds to dispense food into either one of food bowls, and if the message contains an event value to dispense food into both bowls we can run both motors concurrently using the L298N controller.

Here's a video of some timelapse picture captured during the 3 weeks it took to 3D print the feeder.

The Feeder is made up of a small handful of basic hardware components, below is a Breadboard diagram depicting the components used and how they are all wired up together. A regular 12V 2A DC power adapter supply is used to power all the components.

The code to start and stop a motor is about 10 lines of code as shown below. This is the completed version of the Arduino Sketch shown in Part 2 of this blog series when it was partially written at the time.

#include "secrets.h"
#include <WiFiClientSecure.h>
#include <MQTTClient.h>
#include <ArduinoJson.h>
#include "WiFi.h"

// The MQTT topics that this device should publish/subscribe
#define AWS_IOT_PUBLISH_TOPIC   "cat-feeder/states"
#define AWS_IOT_SUBSCRIBE_TOPIC "cat-feeder/action"

WiFiClientSecure net = WiFiClientSecure();
MQTTClient client = MQTTClient(256);

int motor1pin1 = 32;
int motor1pin2 = 33;
int motor2pin1 = 16;
int motor2pin2 = 17;

void connectAWS()
{
  WiFi.mode(WIFI_STA);
  WiFi.begin(WIFI_SSID, WIFI_PASSWORD);

  Serial.println("Connecting to Wi-Fi");
  Serial.println(AWS_IOT_ENDPOINT);

  while (WiFi.status() != WL_CONNECTED) {
    delay(500);
    Serial.print(".");
  }

  // Configure WiFiClientSecure to use the AWS IoT device credentials
  net.setCACert(AWS_CERT_CA);
  net.setCertificate(AWS_CERT_CRT);
  net.setPrivateKey(AWS_CERT_PRIVATE);

  // Connect to the MQTT broker on the AWS endpoint we defined earlier
  client.begin(AWS_IOT_ENDPOINT, 8883, net);

  // Create a message handler
  client.onMessage(messageHandler);

  Serial.println("Connecting to AWS IOT");
  Serial.println(THINGNAME);

  while (!client.connect(THINGNAME)) {
    Serial.print(".");
    delay(100);
  }

  if (!client.connected()) {
    Serial.println("AWS IoT Timeout!");
    return;
  }

  Serial.println("About to subscribe");
  // Subscribe to a topic
  client.subscribe(AWS_IOT_SUBSCRIBE_TOPIC);

  Serial.println("AWS IoT Connected!");
}

void publishMessage()
{
  StaticJsonDocument<200> doc;
  doc["time"] = millis();
  doc["state_1"] = millis();
  doc["state_2"] = 2 * millis();
  char jsonBuffer[512];
  serializeJson(doc, jsonBuffer); // print to client

  client.publish(AWS_IOT_PUBLISH_TOPIC, jsonBuffer);

  Serial.println("publishMessage states to AWS IoT" );
}

void messageHandler(String &topic, String &payload) {
  Serial.println("incoming: " + topic + " - " + payload);

  StaticJsonDocument<200> doc;
  deserializeJson(doc, payload);
  const char* event = doc["event"];

  Serial.println(event);

  feedMe(event);  
}

void setup() {
  Serial.begin(9600);
  connectAWS();

  pinMode(motor1pin1, OUTPUT);
  pinMode(motor1pin2, OUTPUT);
  pinMode(motor2pin1, OUTPUT);
  pinMode(motor2pin2, OUTPUT);
}

void feedMe(String event) {
  Serial.println(event);

  bool feedLeft = false;
  bool feedRight = false;

  if (event == "SINGLE") {
    feedLeft = true;
  }
  if (event == "DOUBLE") {
    feedRight = true;
  }
  if (event == "LONG") {
    feedLeft = true;
    feedRight = true;
  }

  if (feedLeft) {
    Serial.println("run left");
    digitalWrite(motor1pin1, HIGH);
    digitalWrite(motor1pin2, LOW);
  }

  if (feedRight) {
    Serial.println("run right");
    digitalWrite(motor2pin1, HIGH);
    digitalWrite(motor2pin2, LOW);
  }

  delay(10000);
  digitalWrite(motor1pin1, LOW);
  digitalWrite(motor1pin2, LOW);
  digitalWrite(motor2pin1, LOW);
  digitalWrite(motor2pin2, LOW);
  delay(2000);

  Serial.println("fed");
}

void loop() {
  publishMessage();
  client.loop();
  delay(3000);
}

Demo Time​

The Seeed AWS IoT Button is able to detect 3 different types of click events: Long, Single and Double, and we are able to leverage this all the way to the feeder so we will have it performing certains actions base on the click event type.

The video below demonstrates the following scenarios:

• Long Click: this will dispense food into both cat bowls
• Single Click: this will dispense food into Ebok's cat bowl
• Double Click: this will dispense food into Queenie's cat bowl

What's next?​

Build the nervous system of an ultimate nerd project I have in mind that would allow me to voice control actions controlling servos, LEDs and audio outputs, by using a mesh of Seeed XIAO BLE Sense micro-controllers and TinyML Machine Learning.

The source code for this blog can be found in my Github repository: https://github.com/chiwaichan/aws-iot-cat-feeder. This repository only includes the source code for the solution implemented up to this stage/blog in the project.

In the end I decided to go with the Seeed Studio XIAO ESP32C3 implementation of the ESP32 micro-controller for $4.99 (USD). I also ordered some other bits and pieces from AliExpress that's going to take some time to arrive.

In this Part 2 of the blog series I will demonstrate the exchange of messages (JSON payload) using the MQTT protocol between the ESP32 and the AWS IoT Core Service, as well as the exchange of messages between a Lambda Function and the ESP32 - this Lambda is written in Python which is intended to replace the Lambda triggered by the IoT button event found in Part 1.

Prerequisites if you like to try out the solution using the source code​

• An AWS account.
• An IoT button. Follow Part 1 of this blog series to onboard your IoT button into the AWS IoT 1-Click Service.
• Create 2 Certificates in the AWS IoT Core Service. One certificate is for the ESP32 to publish and subscribe to Topics to IoT Core, and the other is used by the IoT button's Lambda to publish a message to a Topic subscribed by the ESP32.

Create a Certificate using the recommended One-Click option.

Download the following files and take note of which device (the ESP32 or the IoT Lambda) you like to use this certificate for:

• xxxxx.cert.pem
• xxxxx.private.key
• Amazon Root CA 1: https://www.amazontrust.com/repository/AmazonRootCA1.pem

Activate the Certificate.

Click on Done. Then repeat the steps to create the second Certificate.

Publish ESP32 States to AWS IoT Core​

The diagram above depicts the components used that is required in order for the ESP32 to send the States of the Cat Feeder, I've yet to decide what to send but examples could be 1.) battery level 2.) Cat weight (based on a Cat's RFID chip and some how weighing them while they eat) 3.) or how much food is remaining in the feeder. So many options.

1. ESP32: This is the micro-controller that will eventually have a bunch of hardware components that we will take States from, then publish to a Topic.
2. MQTT: This is the lightweight pub/sub protocol used to send IoT messages over TCP/IP to AWS IoT Core.
3. AWS IoT Core: This is the service that will forward message to the ESP32 micro-controller that are subscribed to Topics.
4. IoT Topic: The Lambda will publish a message along with the type of button event (One click, long click or double click) to the Topic "cat-feeder/action", the value of the event is subject to what is supported by the IoT button you use.
5. Do something later on: I'll decide later on what to do downstream with the State values. This could be anything really, e.g. save a time series of the data into a database or bunch of DynamoDB tables, or get an alert to remind me to charge the Cat Feeder's battery with a customizable threshold?

Instructions to try out the Arduino/ESP32 part of the solution for yourself​

1. Install the Arduino IDE.
2. Follow this AWS blog on setting up an IoT device, start from "Installing and configuring the Arduino IDE" to including "Configuring and flashing an ESP32 IoT device". Their blog walks us through on preparing the Arduino IDE and on how to flash the ESP32 with a Sketch.
3. Clone the Arduino source code from my Github repository: https://github.com/chiwaichan/aws-iot-cat-feeder
4. Go to the "secrets.h" tab and replace the following variables:

• WIFI_SSID: This is the name of your Wifi Access Point
• WIFI_PASSWORD: The password for your Wifi.
• AWS_IOT_ENDPOINT: This is the regional endpoint of your AWS Iot Core Service.

• AWS_CERT_CA: The content of the Amazon Root CA 1 file created in the prerequisites for the first certificate.
• AWS_CERT_CRT: The content of the xxxxx.cert.pem file created in the prerequisites for the first certificate.
• AWS_CERT_PRIVATE: The content of the xxxxx.private.key file created in the prerequisites for the first certificate.

5. Flash the code onto the ESP32

You might need to push a button on the micro-controller during the flashing process depending on the your ESP32 micro-controller

6. Check the Arduino console to ensure the ESP32 can connect to AWS IoT and publish messages.

7. Verify the MQTT messages is received by AWS IoT Core

Sending a message to the ESP32 when the IoT button is pressed​

The diagram above depicts the components used to send a message to the ESP32 each time the Seeed AWS IoT button is pressed.

1. AWS IoT button: this is the IoT button I detail in Part 1; it's a physical button that can be anywhere in the world where I can press to feed the fur babies once the final solution is built.
2. AWS Lambda: This will replace the Lambda from the previous blog with the one shown in the diagram.
3. IoT Topic: The Lambda will publish a message along with the type of button event (One click, long click or double click) to the Topic "cat-feeder/action", the value of the event is subject to what is supported by the IoT button you use.
4. AWS IoT Core: This is the service that will forward message to the ESP32 micro-controller that are subscribed to Topics.
5. ESP32: We will see details of the button event from each click in the Arduino console once this part is set up.

Instructions to set up the AWS IoT button part of the solution​

1. Take the 3 files create in the second set of Certificate created in the AWS IoT Core Service in the prerequisites, then create 3 AWS Secrets Manager "Other type of secret: Plaintext" values. We need a Secret value for each file. This is to provide the Lambda Function the Certificate to call AWS IoT Core.

2. Get a copy of the AWS code from my Github repository: https://github.com/chiwaichan/aws-iot-cat-feeder

3. In a terminal go into the aws folder and run the commands found in the "sam-commands.text" file, be sure to replace the following values in the commands to reflect the values for your AWS account. This will create a CloudFormation Stack of the AWS IoT Services used by this entire solution.

• YOUR_BUCKET_NAME
• Value for IoTEndpoint
• Value for CatFeederThingLambdaCertName, this is the name of the long certificate value found in Iot Core created in the prerequisites for the second certificate.
• Value for CatFeederThingLambdaSecretNameCertCA, e.g. "cat-feeder-lambda-cert-ca-aaVaa2", check the name in Secrets Manager.
• Value for CatFeederThingLambdaSecretNameCertCRT
• Value for CatFeederThingLambdaSecretNameCertPrivate
• Value for CatFeederThingControllerCertName, this is the name of the long certificate value found in Iot Core created in the prerequisites for the second certificate used by the ESP32.
• Find the Lambda created in the CloudFormation stack and Test the Lambda to manually trigger the event.
• If you have setup an IoT 1-Click Button found in Part 1, you can replace that Lambda with the one created by the CloudFormation Stack. Go to the "AWS IoT 1-Click" Service and edit the "template" for the CatFeeder project.

10. Let's press the Iot Button in the following way:

• Single Click
• Double Click
• Long Click

11. Verify the button events are received by the ESP32 by going to the Arduino console and you should see something like this:

What's next?​

I recently got a Creality3D Ender-3 V2 printer, I've got many known unknowns I know I need to get up to speed with in regards to fundamentals of 3D printing and all the tools, techniques and software associated with it. I'll attempt to print an enclosure to house the ESP32 controller, the wires, power supply/battery (if I can source a battery that lasts for more than a month on a single charge) and most importantly the dry cat food; I like to use some mechanical components to dispense food each time we press the IoT button described in Part 1. I'll talk in depth on the progress made on the 3D printing in Part 3.

If you are forgetful when it comes to feeding your fur babies like me, and you often only realise you need to put some dry food into the bowl when you are at work then you should read these series of blogs. Over time, I'll be designing and building a smart cat feeder over time using a combination of components such as Arduino micro controllers, motors, sensors and IoT devices and Cloud services. I'll publish the steps taken in these series of blogs, also, I'll publish any designs and source code as I figure things out and make decisions on aspects of the design.

In this part 1 of the series, I will do a walkthrough on setting up an AWS IoT 1-Click device to trigger a Lambda Function. I got myself one of these Seeed IoT buttons for $20; I also bought a NCR18650B battery which I realised later on is only required if I wanted to run the device without it being powered by a USB type-C cable (used for charging the power as well).

Firstly, make sure you have an AWS account. Then install the AWS IoT1-Click app onto your phone and log in using your AWS account. With these we will be able to link IoT devices up to our AWS account.

Claim the IoT device with Device ID

Scan the barcode on the back of the device; you can scan multiple devices in bulk.

Next, I'll set up the Wifi on the device so that it can reach the internet internet from home. Can't see why I can't set it up to my phone's AP for feeding on the go, I'll try it out some other time.

Now we'll create a project and add the IoT device to a placement group in the AWS Console. Give a name and description for the project.

Next define a template, this is where we create a Lambda function; all the plumbing between the IoT device and Lambda will be handled for us.

Next we create a placement for the Iot device.

Since I have no Arduino micro-controllers (have yet to buy one), I will get the Lambda to log a message.

Push the button on the Iot device, wait for the event LED status to turn green after flashing white then check the logs CloudWatch Logs.

At some point I have to code the Lambda to perform a real action as each event comes through, which will be demonstrated in a following blog in the series instead of just logging to CloudWatch logs.

Within the app on your phone you can see status of each IoT device such as the remaining battery life percentage.

As well as a history of the button's events.

In the next blog, I'll configure the Lambda to push the event to a Topic for AWS IoT Core to subscribe to, which in turns will trigger an event to an ESP32 ( I've yet to decide on a specific version of the micro-controller) using the IoT MQTT protocol.