[Blog] Enabling Smarter, Safer, and More Efficient Robotics with Lattice FPGAs

Today’s industrial robots are becoming more capable, moving from simple fixed-function systems to perception-driven platforms. Advancements in sensor and processing technology are enabling these systems to better perceive their surroundings, adapt to changing conditions, and make more reliable real-time decisions, if developers can overcome the new processing, power, and latency challenges that come with added complexity.

In our latest LinkedIn Live panel discussion, experts from Lattice, AIRY3D, and MassRobotics met to discuss:

• How advancements in perception, machine vision, sensor fusion, and 3D sensing are driving the next generation of robots.
• How these advancements have led to edge processing and power efficiency problems.
• How Field Programmable Gate Arrays (FPGAs) can help enable these systems as powerful and flexible preprocessing components.

By addressing common obstacles with capable hardware, these robots can become more responsive, and efficient.

The Shift to Perception-Driven Industrial Robotics
Traditionally, industrial robots were designed to complete highly structured, repetitive tasks in controlled environments. Think, for example, of robot arms that pick up, rotate, and place pieces down on an automotive assembly line. More recent advances in technology have enabled developers to move beyond these simple capabilities, building systems that can navigate dynamic environments, interact with humans, and handle a broader range of tasks.

This broadening of roles and functions has made perception – the ability to sense, interpret, and respond to the surrounding environment – a foundational requirement of modern industrial robotic design. As a result, it has dramatically increased both the number and sophistication of sensors used throughout each robotic system.

Beyond conventional machine vision sensors, developers are incorporating things like lidar, depth and touch sensing, and radar into robot designs to enable more sophisticated, autonomous capabilities. Humanoid robotics, for example, are being designed to more closely replicate human perception and interaction in industrial, healthcare, and other human-adjacent settings. They require the real-time coordination of components like motors, vision processors, force-torque sensors, and more to operate effectively and safely.

Achieving this level of real-time perception and coordination is not without its challenges.

Complications of Sensor-Heavy Architecture
Collecting data from these various interconnected sensors is only one part of the equation. A modern industrial robot may combine input from multiple cameras, depth sensors, lidar, touch sensors, and more, each generating unique data in decentralized physical locations. Being able to move, process, and act on this data quickly enough to support autonomous decision-making is no simple task.

And since robots are operating alongside human workers, it is crucial that they operate in a safe and reliable manner. Without rapid perception and decision-making capabilities, these systems will not be able to identify and avert safety risks.

This creates a number of obstacles for the developers of perception-driven industrial robotics, including:

• Navigating depth perception. Tasks like object manipulation, grasping, and pick-and-place operations require precise depth information, especially at or near the end effector. Camera placement constraints, self-occlusion from moving parts, minimum-Z limitations, and other distance-based challenges can inhibit the accuracy of these operations.
• Managing sensor bandwidth. Advanced humanoid robots will handle multiple vision sensors in addition to other sensing modalities. As the number of sensors and sensor-specific data outputs increases, so does the volume of varied data that must be transported and processed in a timely manner.
• Balancing performance and power. When all disparate sensor data is routed to a centralized processor, it creates significant issues with bandwidth, latency, and power consumption. This is exacerbated in space-constrained robotic systems, which are already limited in these regards.

Taken together, these challenges are forcing developers to reassess more traditional robotic architectures. It’s no longer a question of how many sensors they can add to the system, but how they can reliably and efficiently process the vast amount of data these sensors create.

How FPGAs Support Decentralized Architectures
One effective strategy for addressing these challenges is to move processing power closer to the equipment where data is created. Distributed architectures that perform preprocessing, filtering, and other compute-intensive tasks at the edge reduce the strain on centralized systems, improve response times, and enable faster safety-related responses by allowing critical signals to be acted on closer to the sensor.

Low power FPGAs are particularly well-suited for this edge processing role. They can perform a range of critical functions directly alongside cameras, lidar, and other sensors, including:

• Reducing bandwidth requirements by filtering sensor data and transmitting only relevant information to central processors.
• Accelerating perception workloads like depth processing, sensor fusion, object tracking, and region-of-interest detection through programmable edge processing.
• Offloading compute-intensive tasks from central processors, allowing them to focus on higher-level functions like SLAM, motion planning, environmental mapping, and autonomous decision-making.
• Improving power efficiency in constrained environments by avoiding the constant transmission of sensor data throughout the larger system.

Deploying tightly integrated FPGA-based sensing and processing solutions allows developers to deliver timely autonomous responses. Take, for example, the 3D vision solution created by Lattice and AIRY3D. It combines AIRY3D’s single-sensor 3D vision technology with a low power Lattice FPGA platform to enable efficient depth processing directly at the edge. The solution is compact enough for installation near or within robotic end effectors, helping support real-time robotic manipulation tasks while accounting for self-occlusion and compute bottleneck challenges.

The Right Architecture Makes All the Difference
It’s clear that advanced sensor technology alone is no longer enough to support efficient, autonomous, and reliable industrial robotics. Success ultimately depends not on the number of sensors involved, but on where and how perception data is processed. By accelerating critical workloads at the edge, low power FPGAs help translate growing volumes of sensor data into real-time action.

To further explore how Lattice FPGAs can support more responsive and efficient robotics systems at the edge, watch the full LinkedIn Live panel discussion here. For additional information, contact Lattice and explore Lattice’s edge AI or humanoid solutions.