Using AI in Microcontrollers
An Easy Way to Integrate AI Hardware Into Your Business
When you want to integrate artificial intelligence into your business, you’re often told to use an existing piece of software or hardware. While this option is reasonable, it typically fails to meet your needs and expectations. So, why not take artificial intelligence into your own hands with a microcontroller AI project?
In this post, we explore microcontrollers and how you can use them to integrate artificial intelligence into your business. We’ll also advise you on how to get started with AI-integrated projects. Keep reading to learn more.
What Is a Microcontroller?
A microcontroller is a miniature computer that appears to be only a single piece of circuitry. While microcontrollers do not offer the same level of functionality as fully-fledged desktop PCs or laptops, they provide a highly flexible development experience, allowing programmers to create their own hardware with relatively few resources.
There are many types of microcontrollers available. You may have heard of some, but the following are the most popular:
Raspberry Pi: The Raspberry Pi is a credit card-sized computer that runs on Linux-based operating systems. It's used for a wide range of projects, including home automation, media centers, robotics, and as a platform for learning programming and electronics.
Adafruit Trinket: The Adafruit Trinket is a small, low-cost microcontroller board designed for simple projects where space and budget are restricted. It's used in wearable electronics, small-scale automation tasks, and projects where a compact size and low power consumption are critical. It's particularly popular in DIY and educational projects.
ESP32: The ESP32 is a low-cost, low-power system-on-a-chip microcontroller with built-in WiFi and Bluetooth capabilities. It's used for wireless sensor networks, home automation, and applications requiring connectivity to the internet or other devices.
On the software front, microcontrollers run a variety of programming languages, such as C and C++. However, this varies between controllers. For example, the Raspberry Pi can run the full version of Python, while the ESP32 and Adafruit Trinket are restricted to MicroPython or CircuitPython.
AI Microcontroller Projects for Business Use
Microcontrollers have a vast number of potential applications; and with the rise of artificial intelligence, developers can combine the two technologies to create impressive projects for business use. Below, we’ve outlined some potential ways you might integrate AI with a microcontroller of your choice.
Smart Environmental Monitoring System: Utilize AI-enabled microcontrollers to monitor environmental parameters such as temperature, humidity, air quality, and light levels in office spaces, warehouses, or production facilities. AI algorithms can analyze data in real time to provide insights for energy efficiency improvements, predictive maintenance, and indoor air quality optimization.
Predictive Maintenance Systems: Implement AI on microcontrollers to monitor health equipment based on sensor data (vibration, temperature, current, etc.). Predictive maintenance algorithms can analyze historical data to detect anomalies and predict potential failures, allowing businesses to schedule maintenance proactively, reduce downtime, and extend equipment lifespan.
AI-powered Security Systems: Develop innovative security systems using AI microcontrollers to analyze video feeds from security cameras. AI algorithms can detect unusual behavior, recognize faces, and alert security personnel in real time, enhancing overall security and reducing the need for constant human monitoring.
Automated Inventory Management: Deploy AI microcontrollers for inventory tracking and management in warehouses or retail environments. AI algorithms can analyze inventory levels, predict demand based on historical data and external factors, and optimize stock replenishment processes to reduce stockouts and overstock situations.
Customer Insights and Personalization: Use AI microcontrollers to collect and analyze customer data from IoT devices (like smart devices in retail environments or connected products). AI algorithms can process data to generate insights into customer behavior, preferences, and trends. This information can be used to personalize marketing campaigns, improve customer service, and optimize product offerings.
This is not an exhaustive list. The only limit as to what you can do with a microcontroller and AI is your imagination. Even if you have limited technical knowledge, with vast amounts of easily accessible community support, microcontrollers are relatively simple to get started with.
How to Get Started With an AI Microcontroller Project
To get started with your new AI microcontroller project, you’ll first need to determine which controller is right for you. If you’re new to software and hardware development, we recommend using the Raspberry Pi, preferably one with pre-soldered GPIO pins and built-in WiFi and Bluetooth. The Raspberry Pi 5 is an excellent starting point and surprisingly powerful. If this is your first project, we suggest avoiding models like the Pi Zero.
However, if you do have some experience with microcontrollers, the ESP32 may be the right choice. It’s compact yet also comes with Bluetooth and WiFi out-the-box. This controller runs C, C++, and MicroPython. As a result, you’ll need an existing understanding of one of these languages, as community support may be limited. We don’t say this for the Raspberry Pi, as it can be operated like an ordinary PC with a Linux install. Running a fully-fledged operating system on the ESP32 is not possible.
Once you’ve purchased your microcontroller, you’ll need to install a development environment. For the purposes of this article, let’s say you’ve chosen the ESP32 and are using MicroPython. In this case, you’ll need to download the Thonny IDE on your PC or laptop and connect the ESP32 via USB. Then, you can install MicroPython through Thonny.
Next, you should install the appropriate AI libraries. While you may find some MicroPython-compatible libraries on GitHub, you’ll probably need to sign up for a ChatGPT premium account and leverage its API. At this point, the project is already becoming complex. Fortunately, you can follow API documentation to learn how to connect ChatGPT with your controller, or you can hire an AI software developer.
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Very good