Bridging the Sensor Fusion Gap: How FPGA Enhances Real-Time Robotics at the Edge

Bridging the Gap in Sensor Fusion: How FPGA Supports Real-Time Robotics Applications at the Edge

Automation is a cornerstone of modern industrial facilities, with robotics technology serving as a catalyst for its advancement. Currently, AI-driven robotics technology is rapidly developing, leading to larger and more sophisticated industrial deployments. However, as the application scope and scale of automation systems in industrial settings expand, the collection, aggregation, and analysis of sensor data become increasingly challenging. Each additional sensor adds more signals, data, and requirements to the system, consequently increasing risks. Systems that are larger, more complex, and require greater data processing are inherently more prone to errors, delays, and security vulnerabilities. While AI and machine learning (ML) models help streamline robot-driven operations, integrating these models into such systems poses its own challenges. As modern industrial automation systems grow in scale, autonomy, and overall connectivity, the number of potential attack points that hackers can exploit also increases.

The need for sensor fusion is paramount. Reliable operation of automated industrial facilities largely depends on sensor fusion, which involves integrating and processing data from various sensors, devices, and processes. This contextual processing of signals enhances accuracy, visibility, and relevance. Sensor fusion helps optimize and elevate the value of analytical tools and their predictive insights, ensuring minimal downtime while increasing overall throughput and efficiency. Professionals in the contemporary AI and robotics fields recognize that sensor fusion is critical for extending advanced robotic systems to the edge. It is a key enabling factor for achieving real-time responsiveness, with 84% of industry experts considering real-time capability crucial or very crucial for system performance.

When combined with precise motor control, functional safety, and protective measures, sensor fusion addresses many of the critical challenges in designing automated robotic systems. Unfortunately, significant challenges still exist during deployment. For instance, regarding the fusion of cameras and LiDAR sensors, while 75.7% of surveyed industry leaders favor this type of sensor fusion solution, only 67.5% of companies have successfully deployed camera-LiDAR fusion systems. This gap reflects the numerous technical barriers that currently hinder the efficient adoption of robotic automation.

The challenges engineers face, regardless of the specific sensors and AI models involved, require substantial components to support advanced automated robotic applications. Currently, three major technical barriers to implementation remain unsolved:

• Complex Integration of Industrial Robotic Systems: Connecting various advanced sensors performing different tasks is complex. Ensuring the usability of these interconnected system parts requires chip-level flexible input/output (I/O) and high performance, posing significant challenges for many off-the-shelf components. While modern processors utilize advanced process nodes to shrink transistor sizes, enhance performance, and reduce die sizes and costs, this has resulted in more limitations regarding I/O and compatibility with traditional connection needs.
• Digital Twins and Calibration: Many industrial facilities rely on such systems to minimize human errors through high-precision, mission-critical automation. Any desynchronization or connection disruption can have negative effects. Therefore, the internal parameters and physical actions of each robot must precisely match its digital model. Unfortunately, various environmental factors can affect a robot’s operational accuracy, necessitating continuous monitoring and maintenance of calibration.
• Cost and Power Consumption: The initial investment and operational costs for building AI-enabled smart robots hinder widespread adoption of this technology. The specialized sensors required to support such systems are expensive, and the additional energy consumption, computational resources, and model training costs also pose new obstacles. Autonomous robots must also address the challenge of optimizing power consumption while meeting high computational demands.

To facilitate the widespread adoption of AI-assisted robotic technology, designers must find methods to simplify and optimize sensor-based edge architectures without sacrificing speed, computational power, and efficiency. This process should start from the foundational architecture, leveraging dedicated components like field-programmable gate arrays (FPGAs) to explore new solutions for building edge devices.

How FPGAs Support Sensor Fusion

FPGAs have proven to be effective tools for designing and deploying high-performance robotic solutions. They can provide the low latency, synchronization, and deterministic performance required for sensor fusion processing while achieving power levels that conventional processors struggle to match. Additionally, FPGAs can meet core requirements for functional safety, security, and design flexibility, all while being compact and energy-efficient.

However, these features are just the tip of the iceberg regarding their potential value to the large-scale implementation of AI-driven robotics automation. FPGAs offer a high-quality solution to the primary challenges of sensor fusion due to their unique composite capabilities. The core advantage of these chips lies in their ability to perform parallel processing, allowing for simultaneous execution of multiple tasks. By concurrently handling signal processing, alignment, sensor fusion, and integrating computer vision with edge AI, FPGA chips offload certain tasks from the main computing components. This reduces system latency and processing pressure while expanding the operational capabilities of the devices.

This capability can significantly accelerate the processing speed of critical tasks, enhancing the accuracy and decision-making efficiency of robotic systems and ultimately enabling more reliable, stable, precise, and efficient real-time operations. FPGAs also address the aforementioned I/O-computational power dilemma by providing highly customizable I/O and flexible protocol support. This allows them to interoperate with various sensors and actuators that adhere to common standards such as Ethernet, SPI, LVDS, CAN, MIPI, JESD-204B, and GPIO. By minimizing latency and offering deterministic low-power processing capabilities while sharing the workload of sensor fusion, computer vision, and physical AI, these chips help solve common computational and power consumption challenges, thereby enhancing overall system performance and expanding operational capabilities.

As the name suggests, these semiconductors not only offer flexibility during the design phase. FPGAs can be updated post-deployment, addressing a commonly overlooked barrier: changes in future requirements. Their reprogrammable nature further extends the potential for advancing robotic automation technologies into new phases, enabling iterative upgrades to adapt to emerging needs while prolonging the effective lifespan of devices.

Current and Future Outlook

As the demand for real-time data processing and decision-making grows, simplifying the integration and management of sensor data will become critical for the successful implementation of robotics automation and system risk management. FPGAs lay a solid foundation for advancing these efforts, providing designers with sufficient flexibility to optimize sensor fusion solutions while redefining the roles that intelligent robots can play in current and future industrial production.

When combined with other advanced components, FPGAs will help lead the next generation of robotics and automation deployments, continuing to offer flexible support as the field matures. They exemplify that despite numerous challenges, intelligent, automated, and real-time industrial robotic solutions are within reach.