Physical artificial intelligence (AI) is rapidly shifting expectations for industrial robotics. Autonomous mobile robots (AMRs), collaborative robots (cobots), intelligent vision systems, and other autonomous machines must increasingly negotiate complex real-world situations, using advanced perception and adaptive reasoning driven by low-latency local intelligence.
The combination of AI, sensing, and physical interaction places new demands on embedded edge computing platforms. But for robotics OEMs, underlying architectures must also support the journey from early development to industrial-scale deployment of heterogeneous machinery.
An AMR, for example, may prioritize power-efficient AI processing, cameras, LIDAR, and wireless connectivity, while a cobot may require a different combination of machine vision, industrial networking, and deterministic real-time motor control capabilities. Even within the same product family, processing and I/O requirements can vary considerably.
As system requirements continue to evolve, designing new computing platforms around every individual machine becomes increasingly difficult to justify. Modular compute offers a scalable solution that helps support extended industrial lifecycles and future product development.
Why Modular Compute Matters for Physical AI and Robotics
For embedded computing architectures, processing resources, memory, and I/O are often closely tied to specific application requirements. If any of these requirements change, upgrading the underlying system architecture can be considerably more complex than simply selecting a newer processor.
Physical AI often requires additional processing resources for efficient edge AI, more memory capacity and bandwidth to support increasingly complex vision systems, and new I/O for modern sensors and networking. However, the fundamental challenge for engineers is that these requirements continue to rapidly increase.
By shifting to modular system architectures, engineers can separate computing resources from application-specific hardware and reduce upgrade difficulties when system requirements change.
During prototyping, engineers can establish the processing, sensing, and AI requirements of the application before committing to production hardware. When designing production models, application-specific I/O can then be developed around an appropriate modular computing platform, which can potentially be reused across different product variants and future product generations.
Modularity is therefore about more than making one component replaceable. For industrial OEMs developing multiple intelligent machines, it allows the computing architecture itself to become a reusable part across long-term product roadmaps.
Building Scalable Robotics Platforms with SOMs and Open COM Standards
System-on-modules (SOMs) and computer-on-modules (COMs) provide a practical way of separating computing platforms from application-specific product design.
Core computing resources, such as the processor and memory, are integrated into the module, while a carrier board provides the interfaces and other functionality required by the specific machine. For industrial robotics, this allows cameras, sensors, motor controllers, networking interfaces, and other hardware to remain closely aligned with individual machine requirements without tying designs to specific processor implementations.
By integrating compute through carrier-compatible modules, designers can accommodate changes in processing performance, power requirements, and AI capabilities without having to completely redesign the computing platform or surrounding application hardware. It also allows OEMs to adapt intelligent machines to changing processing requirements without treating every new generation as an entirely new design.
Open COM standards support this approach through standardized module architectures that can simplify compute integration and support faster time to market. They also provide interoperability between compatible solutions, which can help reduce vendor lock-in for long-term supply chain resilience.
Open COM-based design is already being applied to industrial physical AI systems through SOMs powered by advanced Qualcomm processing platforms. SECO's portfolio includes solutions based on the Smart Mobility Architecture (SMARC) standard, such as SOM-SMARC-Dragonwing-IQ8 and SOM-SMARC-QCS6490, which offer high-performance heterogeneous processing within a compact form factor and narrow power envelope. Alternatively, SOM-COMe-CT6-Dragonwing-IQ-X is based on the COM Express Type 6 Compact standard, which provides extensive high-speed I/O and supports higher-performance computing configurations.
Furthermore, SOM-SMARC-Dragonwing-IQ8 uses the same Qualcomm Dragonwing IQ8 processing family as the Arduino VENTUNO Q development platform, which is specifically targeted towards AI and robotics applications. As such, it provides a production-oriented platform that helps reduce redevelopment when transitioning from physical AI prototypes based on VENTUNO Q.
Extending Modularity into the Software Stack
Industrial robotics platforms depend on robust operating systems, drivers, AI frameworks, security functions, and management software. But the benefits of hardware modularity are arguably significantly reduced if underlying hardware changes require significant software redevelopment. Software architecture therefore plays a key role in effective modular computing strategies.
The Yocto Project provides a configurable, modular foundation for creating highly custom embedded Linux environments. Rather than using a general-purpose operating system (OS), developers can build and maintain software images around the specific packages, drivers, and services required by the application. This allows developers to cater to a wider set of processing platforms while omitting unnecessary software components and reducing overall attack surface.
The SECO Clea software framework takes the core benefits of Yocto Linux and provides an edge-to-cloud ecosystem that is compatible with many third-party industrial systems. Through Clea OS and its related software modules, SECO enables developers to support long-term cybersecurity and software lifecycle management across centralized heterogeneous fleets. AI enablement tools such as SECO App Hub then empower developers to deploy validated AI models at the edge across multiple supported compute architectures.
The result is a broader definition of modularity. Rather than focusing solely on whether one computing module can replace another, SECO demonstrates how hardware, operating systems, applications, and management infrastructure can work together as reusable platforms within physical AI deployments.
Building Compute Platforms for the Next Generation of Intelligent Machines
AMRs and cobots demonstrate the importance of flexible, modular compute. Both applications fall within the wider move towards physical AI, yet the computing architectures required by each can vary significantly. The same is true for intelligent vision systems, autonomous industrial equipment, and emerging robotics applications.
A modular architecture is not intended to make these machines identical. It instead provides a common foundation that can be adapted to individual machine requirements. For industrial robotics OEMs, modularity allows engineers to consider how current architecture might support different performance levels, new AI workloads, and future products through later upgrades.
As physical AI continues to develop, processors, sensors, and software are likely to advance more quickly than many industrial machines themselves. Separating these technologies from application-specific hardware can help OEMs create intelligent machines designed to evolve, rather than requiring a ground-up redesign.
Talk to our experts to explore how SECO modular hardware and software platforms can support the next generation of industrial physical AI and robotics systems.