The convergence of IoT (Internet of Things), robotics, and artificial intelligence (AI) is driving innovations across multiple industries, from manufacturing to healthcare, and even smart cities. AI applications often require specific hardware to process data efficiently and in real-time, making the choice of sensor or board crucial. In 2024, selecting the optimal IoT or robotics sensor or board for AI applications requires a balance between computational power, energy efficiency, and integration capabilities. Below are some of the leading sensors and boards in 2024 that offer strong support for AI applications:

1. NVIDIA Jetson Series

The NVIDIA Jetson platforms have long been recognized as one of the best choices for AI and robotics applications due to their powerful GPUs and high-performance capabilities. The Jetson family, including the Jetson Nano, Jetson Xavier NX, and Jetson AGX Orin, provides an optimal environment for deploying AI algorithms in edge devices, like robotics and IoT applications.

Key Features:
- High-performance GPUs tailored for AI and deep learning applications.
- Low-power consumption, ideal for edge computing.
- Support for popular AI frameworks such as TensorFlow, PyTorch, and OpenCV.
- Compact and scalable, making it perfect for robotics and IoT devices.
Use Cases:
- Autonomous robotics.
- Computer vision applications.
- Edge AI for smart cities and industrial automation.

2. Raspberry Pi 4 with AI Add-ons

The Raspberry Pi 4, paired with AI-focused add-ons like the Google Coral USB Accelerator, is an affordable yet capable platform for IoT and robotics AI applications. While the Raspberry Pi alone isn’t as powerful as Jetson, its accessibility, ease of use, and compatibility with a wide range of sensors make it an ideal choice for AI developers on a budget.

Key Features:
- Quad-core ARM Cortex-A72 processor for general-purpose computing.
- GPIO pins for interfacing with a wide variety of sensors.
- The Coral USB Accelerator provides TensorFlow Lite acceleration for edge AI tasks.
Use Cases:
- IoT devices with light AI tasks.
- Edge computing for AI in smaller projects.
- DIY robotics and AI experiments.

3. Intel Neural Compute Stick 2

Intel’s Neural Compute Stick 2 (NCS2) is designed to bring deep learning capabilities to edge devices. It’s a USB device that connects to any host system and accelerates AI workloads locally. It’s based on the Intel Movidius Myriad X VPU, which is optimized for AI and vision tasks, making it ideal for IoT devices and robotics applications that require image recognition, object detection, or facial recognition.

Key Features:
- AI acceleration on the edge with support for deep neural networks.
- Plug-and-play USB device that integrates with many systems, including Raspberry Pi and other SBCs.
- Energy-efficient and designed for edge AI inference.
Use Cases:
- Object detection and computer vision tasks in robotics.
- Edge-based AI for IoT security systems.
- Low-latency AI applications.

4. Arduino Portenta H7

The Arduino Portenta H7 is a high-performance board that offers dual-core processing with the option to run tasks on either an ARM Cortex-M7 or ARM Cortex-M4. It’s equipped with a range of sensors and can seamlessly integrate with AI frameworks. With its flexibility, the Portenta H7 is excellent for both IoT and robotics applications requiring real-time data processing and machine learning tasks.

Key Features:
- Dual-core processor (Cortex-M7 and Cortex-M4) for flexibility.
- Built-in Wi-Fi and Bluetooth for seamless IoT communication.
- AI and machine learning capabilities with integration to TensorFlow Lite.
- Powerful enough for various IoT and robotics applications.
Use Cases:
- Smart wearables and healthcare devices.
- Real-time edge AI for robotics.
- IoT-based smart home systems.

5. SparkFun Edge Development Board

The SparkFun Edge Development Board is designed for machine learning on the edge and is ideal for AI-powered IoT devices. It uses a Nordic Semiconductor nRF52840 SoC and a TensorFlow Lite for Microcontrollers setup, making it well-suited for low-power, real-time AI applications.

Key Features:
- Low-power, energy-efficient design.
- TensorFlow Lite for Microcontrollers support for on-device inference.
- Built-in sensors for motion and environmental data gathering.
- Designed for low-latency, real-time processing.
Use Cases:
- Wearables and health-monitoring devices.
- AI-powered environmental monitoring.
- Low-power IoT sensors for predictive maintenance.

6. Bosch BME680 Environmental Sensor

The BME680 is an integrated environmental sensor that provides readings for temperature, humidity, pressure, and gas. It’s an excellent choice for AI applications requiring environmental sensing and can be used in combination with boards like Raspberry Pi or Arduino to create IoT devices capable of predictive analytics, anomaly detection, and smart environmental control.

Key Features:
- Measures temperature, humidity, pressure, and gas concentrations.
- Compact and energy-efficient for edge AI applications.
- Works seamlessly with platforms like Raspberry Pi for environmental monitoring.
Use Cases:
- Environmental sensing in smart homes and buildings.
- Air quality monitoring for AI-powered health applications.
- Smart agriculture and weather prediction systems.

7. LIDAR Sensors (e.g., RPLIDAR A1)

LIDAR (Light Detection and Ranging) sensors, such as the RPLIDAR A1, are becoming essential for robotics and autonomous vehicle applications. They enable 360-degree scanning for precise mapping, obstacle detection, and navigation, which is crucial for AI-driven robotics applications, particularly in environments where computer vision isn’t enough.

Key Features:
- High-precision 360-degree scanning.
- Ideal for navigation and environment mapping.
- Works well with robotic platforms like ROS (Robot Operating System).
Use Cases:
- Autonomous robotics and drones.
- Autonomous vehicles for navigation and obstacle avoidance.
- Mapping and environmental scanning.

Conclusion: Choosing the Right Sensor or Board

In 2024, selecting the right IoT or robotics sensor or board for AI applications depends on your specific needs:

For heavy AI processing on the edge, the NVIDIA Jetson series remains the most robust option.
For lower-power tasks and more budget-friendly solutions, Raspberry Pi 4 with AI add-ons or Arduino Portenta H7 are excellent choices.
For specialized AI tasks like object detection and image recognition, integrating the Intel Neural Compute Stick 2 with a compatible board provides powerful edge AI acceleration.
For environmental monitoring, the Bosch BME680 and LIDAR sensors are key for data gathering.

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