AI boards are crucial in robotics, providing the processing power needed for complex tasks such as object recognition, data processing, and real-time decision-making. These small but powerful computers have enabled robots to operate in dynamic environments with increasing autonomy. With robotics growing in popularity, a range of AI boards now caters to different budgets, skill levels, and project needs. From entry-level options to high-powered boards for advanced applications, AI boards offer various levels of processing, connectivity, and software support, allowing developers to choose a board that aligns with their project requirements.
Why AI Boards are Essential in Robotics
AI boards enhance robotic capabilities by enabling them to perform advanced tasks like computer vision, machine learning, and data processing. Unlike basic command-following machines, AI-powered robots can adapt, learn from data, and make real-time decisions, greatly expanding their range of possible applications.
In healthcare, these capabilities support robots that assist in surgeries and patient care, improving precision and reducing human error. In manufacturing, AI boards power robots that enhance automation, from assembly tasks to quality control. In education, AI boards introduce students to hands-on learning with responsive robots, making robotics more accessible and interactive. By equipping robots with such capabilities, AI boards increase efficiency, precision, and adaptability across fields, advancing the role of robotics in solving complex problems.
Top AI Boards for Robotics
Choosing the right AI board is crucial in building an efficient and capable robot. Here’s a look at some of the top AI boards for robotics, each with unique features that make it ideal for different kinds of robotics projects. For a comprehensive look at leading AI boards and their features, you may also want to check out this guide on the top AI boards for robotics.
We’ll discuss what makes each board special and explore the best use cases for each.
NVIDIA Jetson Nano Developer Kit
A powerful choice for tasks requiring high computing power, such as computer vision and deep learning. It features an NVIDIA Maxwell GPU and quad-core ARM Cortex-A57 CPU, handling intensive tasks like image recognition with ease. The Jetson Nano is also compatible with popular AI frameworks like TensorFlow, making it ideal for machine learning projects.
- Features: NVIDIA Maxwell GPU, quad-core ARM Cortex-A57 CPU
- Benefits: High computing power, compact, strong GPU for deep learning
- Ideal Use Cases: Computer vision, deep learning, autonomous robots
Raspberry Pi 4 Model B
Known for affordability and flexibility, the Raspberry Pi 4 Model B is a popular choice for a wide range of projects. Equipped with a quad-core ARM Cortex-A72 CPU, it offers solid performance for entry-level to intermediate tasks. Available in multiple RAM configurations, it suits various project needs and is backed by a large community for support and resources.
- Features: Quad-core ARM Cortex-A72 CPU, 1GB to 8GB RAM options
- Benefits: Cost-effective, flexible, extensive community support
- Ideal Use Cases: Educational projects, DIY robotics, budget-friendly applications
Arduino Nano RP2040 Connect
Combining the Arduino platform with AI functionality, this board is well-suited for sensor-based and interactive robotics. It includes dual ARM Cortex-M0+ cores and an onboard ESP32 module, providing wireless connectivity. Built-in sensors such as an accelerometer and microphone allow responsive behavior, making it ideal for IoT and interactive projects.
- Features: Dual-core ARM Cortex-M0+, ESP32 for Wi-Fi/Bluetooth
- Benefits: Wireless communication, sensor integration, Arduino IDE compatibility
- Ideal Use Cases: Sensor-driven robots, IoT projects, interactive applications
Intel Neural Compute Stick 2
An AI accelerator that boosts the processing power of compatible devices like the Raspberry Pi. With its Intel Movidius Myriad X VPU, it enhances AI capabilities for tasks like object detection and image classification without requiring a full hardware upgrade.
- Features: Intel Movidius Myriad X VPU, USB connectivity
- Benefits: AI processing boost, affordable, enhances compatible devices
- Ideal Use Cases: Advanced AI tasks, image recognition, robotics needing AI augmentation
Particle Argon IoT Development Board
Ideal for IoT-based robotics, the Particle Argon board features dual processors, supporting Wi-Fi and Bluetooth. It also includes OpenThread mesh networking, enabling communication between multiple devices. Integration with Particle Cloud allows for remote updates and control, useful in connected robotics systems.
- Features: ESP32 and nRF52840 processors, Wi-Fi/Bluetooth, OpenThread
- Benefits: IoT connectivity, cloud integration, remote updates
- Ideal Use Cases: IoT robotics, networked systems, industrial automation
Factors to Consider When Choosing an AI Board for Robotics
Selecting the right AI board is crucial to the success of your robotics project. Here are some of the key factors to consider when choosing an AI board that aligns with your project’s requirements and goals.
- Processing Power: Processing power is critical, especially for complex projects involving real-time data processing, machine learning, or computer vision. Boards like the NVIDIA Jetson Nano, with its powerful GPU, are excellent for tasks that require high computing capacity, while more lightweight tasks can be managed efficiently by boards like the Arduino Nano RP2040, which balances functionality and energy efficiency.
- Connectivity: Connectivity options such as Wi-Fi, Bluetooth, and Ethernet are essential for projects that rely on network integration, remote operation, or IoT applications. Boards like the Particle Argon, which supports multiple connectivity options, are suitable for projects requiring seamless communication between devices or cloud-based interactions.
- Power Consumption: For mobile robots, energy efficiency is a crucial factor, especially in battery-powered designs. High-processing boards may consume more power, impacting battery life. Options like the Raspberry Pi 4 offer a balance of performance and energy efficiency, making them ideal for mobile projects that require extended operational periods without frequent recharging.
- Cost: Budget constraints are often a major consideration. Boards like the Raspberry Pi are known for their affordability, catering to hobbyists and entry-level projects. However, for projects demanding more advanced AI capabilities, the investment in a board like the NVIDIA Jetson Nano may be worthwhile due to its high processing power.
- Software Support: Strong software support and community resources can significantly ease development and troubleshooting. Boards with extensive libraries and development tools, such as the Jetson Nano and Raspberry Pi, are well-supported, offering compatibility with popular frameworks and IDEs. Access to a large community provides valuable resources, making these boards ideal for both beginners and professionals.
Conclusion
Choosing the right AI board is fundamental to the success of any robotics project. Factors like processing power, connectivity options, power consumption, cost, and software support should align with your project’s specific goals. For more information on AI boards and to purchase components suited to your needs, consult trusted robotics suppliers like Think Robotics, which offers a range of AI boards and related components to support your robotics journey.