Robotics is revolutionizing industries, from manufacturing to healthcare, and embedded systems are at the heart of this transformation. But what exactly is an embedded system, and why is it so crucial in robotics? Whether you’re a hardware engineer, a robotics developer, or someone exploring SBCs and SoMs, this article will walk you through everything you need to know about embedded systems in robotics.
Understanding Embedded Systems in Robotics
How Embedded Systems Work in Robotics
An embedded system is a compact, purpose-built computing unit designed to perform specific tasks efficiently. Unlike general-purpose computers, embedded systems are optimized for real-time performance, low power consumption, and seamless integration with hardware components.
In robotics, embedded systems handle critical functions like sensor processing, motor control, artificial intelligence (AI) computation, and communication with other devices. These systems are embedded directly into robots, ensuring they can process data quickly and operate autonomously without relying on external computers.
For example, an autonomous robot navigating a warehouse uses an embedded system to process sensor data, make decisions, and execute movements—all in real-time. Without embedded systems, robots would lack the speed and intelligence required for complex tasks.
Why Are Embedded Systems Essential for Robotics?
Embedded systems bring several advantages to robotics, making them indispensable in modern automation. Here’s why:
1. Real-Time Processing for Autonomous Functions
Robots need to react instantly to changes in their environment. Whether it’s a self-driving car detecting obstacles or a robotic arm assembling products, embedded systems enable real-time data processing to ensure smooth and precise operations.
2. Power Efficiency and Compact Design
Unlike bulky desktop computers, embedded systems are small and power-efficient, making them ideal for robots with space and energy constraints. This is particularly important in battery-powered robots, such as drones and mobile robots, where every watt of power counts.
3. Seamless Integration with AI and Machine Learning
Modern robots rely on AI and machine learning (ML) to improve decision-making. Embedded systems equipped with high-performance GPUs and NPUs (Neural Processing Units) enable robots to analyze images, recognize patterns, and perform AI-driven tasks on the edge without needing cloud computing.
4. Customization for Specific Robotics Applications
Unlike standard computers, embedded systems can be customized to meet the specific needs of a robot. Engineers can tailor hardware components, optimize software, and fine-tune real-time processing to create highly specialized robotic solutions.
Choosing the Right Embedded System for Robotics
Selecting the right embedded system is critical to ensuring a robot operates efficiently and meets project requirements. Here’s what you need to consider:
SoM vs. SBC – Which One Should You Choose?
When choosing an embedded system for robotics, you’ll typically encounter two options:
1.System-on-Module (SoM): A modular computing unit that integrates a CPU, GPU, RAM, and storage onto a compact board. SoMs require a carrier board for full functionality. They are ideal for custom robotics solutions where flexibility and scalability matter.
2.Single-Board Computer (SBC): A self-contained computer with all essential components on a single board. SBCs are plug-and-play solutions that are great for rapid development and prototyping.
Which one should you pick?
•If you’re building a custom robotic system with unique I/O requirements, go for a SoM.
•If you need a ready-to-use embedded computer for robotics research or small-scale applications, choose an SBC.
Recommended Embedded Systems for Robotics Development
For AI-powered robotics, choosing an embedded system with strong GPU and NPU capabilities is crucial. Some of the best options include:
• NVIDIA Jetson Orin NX – Ideal for AI-driven robotics, featuring Ampere GPU architecture and up to 100 TOPS of AI performance.
•NVIDIA Jetson Xavier NX – A compact yet powerful solution for machine vision, deep learning, and real-time robotics.
•Rockchip RK3588-based SBCs – A cost-effective alternative for AI applications with powerful multi-core performance.
Each of these platforms provides high-speed processing, rich I/O interfaces, and support for AI frameworks, making them perfect for robotics applications.
The Future of Embedded Systems in Robotics
As technology advances, embedded systems in robotics will continue to evolve. Here’s what we can expect in the near future:
Advancements in AI-Powered Robotics
Robots are becoming more intelligent, thanks to the integration of advanced AI models. Future embedded systems will feature even more powerful AI accelerators, allowing robots to perform complex decision-making, natural language processing, and advanced object recognition with minimal latency.
The Role of Edge Computing and 5G in Embedded Systems
The combination of edge computing and 5G will transform how embedded systems operate in robotics. Instead of relying on cloud servers, robots will process AI workloads locally on powerful embedded systems, reducing latency and improving efficiency.
For example, autonomous delivery robots will use 5G connectivity and edge AI to navigate busy streets safely and make real-time decisions.
Conclusion: Why Embedded Systems Are the Backbone of Robotics
From self-driving cars to industrial automation, embedded systems enable the intelligence, efficiency, and real-time processing that modern robots need. Choosing the right embedded platform—whether it’s an SoM or SBC—can determine the success of a robotics project.
As AI and edge computing continue to advance, embedded systems will become even more powerful, efficient, and widely used in robotics. Whether you’re a hardware engineer, a robotics developer, or a tech enthusiast, understanding embedded systems is key to staying ahead in the world of robotics.