The rapid advancements in artificial intelligence (AI) have opened up new possibilities for IoT (Internet of Things) and robotics applications. In 2024, AI is becoming increasingly integrated into a variety of industries, from healthcare and manufacturing to smart homes and autonomous vehicles. But one of the critical components driving these innovations is the sensors and boards that enable IoT devices and robots to interact with the physical world.

Choosing the right sensor or board for AI applications is crucial to ensure that data is accurately collected, processed, and used for intelligent decision-making. With so many options available, it’s important to consider factors like processing power, connectivity, power consumption, and sensor capabilities when selecting the optimal hardware for AI-powered systems.

1. Raspberry Pi 4: A Versatile and Affordable Board

The Raspberry Pi 4 remains one of the most popular and widely used boards for AI and IoT applications. Known for its affordability and versatility, it offers a great balance between performance and cost, making it an excellent choice for developers looking to implement AI in robotics and IoT devices.

Processing Power: The Raspberry Pi 4 is equipped with a quad-core ARM Cortex-A72 processor, which offers enough computational power for running machine learning models locally.
AI Integration: It supports AI frameworks such as TensorFlow Lite and OpenCV, enabling developers to deploy lightweight AI models for tasks like image recognition, voice processing, and predictive analytics.
Connectivity: With built-in Wi-Fi and Bluetooth, the Raspberry Pi 4 allows easy integration with other IoT devices and cloud platforms for more complex AI tasks.
Expandability: The Raspberry Pi has numerous GPIO pins for adding sensors like temperature, humidity, and motion, making it an ideal base for various IoT applications.

2. NVIDIA Jetson Nano: AI-Powered Robotics Platform

The NVIDIA Jetson Nano is another strong contender for AI-driven IoT and robotics applications. As part of NVIDIA’s Jetson platform, the Nano offers powerful GPU acceleration, making it particularly well-suited for running AI models that require high computational power, such as deep learning and computer vision tasks.

AI-Centric Hardware: The Jetson Nano features a 128-core Maxwell GPU, which delivers significant performance for AI models that process visual and sensor data in real-time.
Power Efficiency: Despite its powerful GPU, the Jetson Nano is highly power-efficient, making it suitable for mobile or battery-powered IoT devices.
Versatile I/O: With support for a wide range of sensors and cameras, including the popular Raspberry Pi camera, the Jetson Nano can be easily integrated into robotics systems that require computer vision and object recognition.
Software Support: It supports a variety of AI frameworks like TensorFlow, PyTorch, and Caffe, enabling seamless deployment of machine learning models for real-time applications.

3. Arduino Portenta H7: Ideal for Edge AI

For developers looking for a board with an emphasis on edge AI, the Arduino Portenta H7 is an excellent option. Designed to handle both low-power IoT tasks and more complex AI workloads, the Portenta H7 is capable of running advanced machine learning models locally, reducing the need for cloud-based computation.

Dual-Core Processor: The board features a dual-core ARM Cortex-M7 and Cortex-M4 processor, providing a balance between real-time processing and complex AI tasks.
Edge AI: With its ability to run AI models at the edge, the Portenta H7 is well-suited for robotics applications where low latency and real-time decision-making are essential, such as in autonomous drones or smart robots.
Connectivity and Sensors: The board supports various connectivity options like Wi-Fi, Bluetooth, and cellular, along with expansion capabilities for connecting sensors like accelerometers, gyroscopes, and cameras.
Low Power Consumption: This board is optimized for low-power use, making it ideal for IoT devices that require long battery life.

4. Intel Neural Compute Stick 2: AI Accelerator for IoT Devices

The Intel Neural Compute Stick 2 is a USB-based AI accelerator that enhances the capabilities of edge devices by providing additional computational power specifically designed for running AI models. It’s a great choice for developers who want to add AI capabilities to an existing IoT or robotics platform without having to switch out the entire board.

AI Processing Power: The Compute Stick features the Intel Movidius Myriad X VPU (Vision Processing Unit), designed to accelerate neural network inference on edge devices, making it perfect for real-time AI applications.
Compact Design: Its small form factor allows it to be easily integrated into existing hardware setups like the Raspberry Pi or other embedded systems.
Software Support: The Compute Stick is compatible with popular AI frameworks like TensorFlow, Caffe, and OpenVINO, enabling smooth integration of pre-trained models for tasks like object detection and facial recognition.

5. Sensor Selection for AI-Driven IoT Applications

Choosing the right sensors to pair with your IoT or robotics platform is just as important as selecting the right board. For AI applications, sensors must provide accurate, high-quality data that can be used by machine learning models to make decisions. Here are some of the best sensors for AI-driven systems:

LiDAR Sensors: For applications like autonomous vehicles or robotic navigation, LiDAR sensors provide 3D mapping capabilities that are critical for real-time decision-making.
Cameras and Computer Vision Sensors: High-quality cameras, such as those based on the Raspberry Pi Camera or the Intel RealSense depth camera, are essential for computer vision tasks in robotics.
Temperature and Humidity Sensors: In IoT applications, sensors like the DHT11 or DHT22 can provide environmental data for monitoring systems, predictive maintenance, or climate control.
Motion and Proximity Sensors: Used in robots for navigation or human-robot interaction, sensors like Ultrasonic or Infrared sensors help detect obstacles or track movement.

Conclusion: Choosing the Right IoT/Robotics Sensor or Board for AI in 2024

In 2024, the optimal sensor or board for AI applications will depend on the specific needs of the project, such as processing power, power efficiency, sensor types, and connectivity options. Boards like the Raspberry Pi 4, NVIDIA Jetson Nano, and Arduino Portenta H7 are excellent choices, each offering distinct advantages in terms of processing power, AI support, and expandability.

Which IoT/Robotics Sensor or Board is Optimal for AI Applications in 2024?