It’s been fascinating to see how the robotics industry has changed over the last decade. Ten years ago it was stuck in a recurring loop, a typical example being a robotic arm on a car assembly line endlessly spot‑welding the same joint on every chassis that rolled past. The fusion of AI with robotics changed all of that. Now it’s more likely to be an autonomous mobile robot in a warehouse that uses LiDAR, cameras, IMUs and AI‑driven navigation to cherry‑pick products from shelves, its onboard systems performing split‑second sensor fusion to reconcile inputs from multiple data sources in real time.
While AI and machine learning provided the catalyst for the change, the enabler has been FPGAs with their ability to deliver deterministic, ultra‑low‑latency processing for multiple data streams in parallel, something conventional processors often struggle to match in the time‑critical, safety‑critical world of robotics. For mobile platforms, that processing comes with another advantage: efficiency. FPGAs can deliver GPU‑class acceleration at a fraction of the power budget, significantly extending battery life.
The capabilities of a robot can also evolve through in‑field updates, where a validated software package or partial hardware reconfiguration is deployed with minimal disruption, often over‑the‑air, at a specific time. This enables the adaptation to new roles or technologies, and supports the long‑term availability of industrial‑grade devices which can remain in production for a decade or more.
That’s the promise, but it brings challenges when developing robotic applications using FPGAs. The hardware-software handshake has to cope with a constantly shifting mix of workloads, all running in real time on a moving platform. Alongside this there’s the requirement to deliver microsecond‑level response times within tight power and size limits, optimizing for efficiency while meeting rigorous safety and security requirements. Engineers also need to architect designs so that individual IP blocks, whether motor controllers, sensor interfaces, or communications protocols, can be swapped or upgraded while guaranteeing that interfaces, timing, and safety‑critical functions remain stable.
Modern SoC FPGA toolchains have matured to support this approach with modular workflows, simulation, and co‑design environments that make it easier to validate changes before deployment. This modularity brings other dividends too. Debugging is faster when a misbehaving block can be isolated and replaced without ripple effects. Scalability improves because the same building blocks can be recombined for different classes of robot. Development cycles shorten as proven IP and code are lifted from one project to the next, confident that interface contracts will hold. Security can also be embedded at the hardware level, guarding against tampering or unauthorized updates in the field.
These aren’t abstract benefits; they’re the core of what will be explored in depth in a session at the FPGA Horizons conference on October 7. Robot on Chip: How an Altera SoC FPGA and Associated Toolchain Can Be Used for Highly Integrated Robotics Designs will see Adam Titley, Altera’s Director of Robotics & Network Technologies, and Scott Ware, Senior Software Engineer, unpack the benefits of modular hardware and software design. They’ll show how it supports rapid prototyping and adaptation to various robotic platforms, from mobile robots to manipulators. Through case studies and demonstrations, they’ll also illustrate how this approach promotes efficient reuse, robust safety and security, seamless system integration, and long‑term maintainability in complex robotics projects across both research and industry.
Photo by Erhan Astam on Unsplash
