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Application
Robotics Solutions Stack
Hardware, software, and IP for machines that sense, think, and act in real time.
Shaping the Future of Robotics Solutions
Physical AI is moving intelligence from the cloud and data center into machines that sense, decide, and act in the real world. Robots, autonomous mobile platforms, drones, and intelligent factory equipment now combine more sensors, local inference, networking, and control than a conventional fixed-function controller was designed to manage. These systems need high-performance AI alongside predictable sensor processing, data movement, networking, and control.
The Altera® Robotics Solutions Stack is a layered framework of hardware, software, and intellectual property (IP) blocks that together enable a robot to sense, think, and act. It integrates components such as sensors, AI processors, communication interfaces, motor controllers, and functional safety modules to provide a complete end-to-end solution for building autonomous robots, drones, and industrial automation systems. The stack ensures that data flows efficiently from perception (sensing) to computation (thinking) to actuation (motion/control), reducing development complexity and accelerating deployment.
The Challenges, and Why FPGA
The Challenge
High-Rate Sensor Streams
Sensor streams need aggregation, timestamping, conversion, filtering, and efficient delivery to downstream compute.
Why FPGA
Parallel Pipelines
Filtering, image conditioning, sensor fusion, and data movement run side by side in hardware rather than queuing for processor time, so high-rate sensor data arrives structured and time-aligned.
The Challenge
Bounded Timing for Motion and Safety
Motion and safety-related functions need bounded timing even while AI workloads vary in demand.
Why FPGA
Predictable Execution
Control and safety-related paths that require bounded behavior stay in dedicated hardware, separated from the scheduling variability of software running alongside them.
The Challenge
Long Product Life, Changing Requirements
Robot variants, sensors, protocols, and safety cases all change across a long product life, favoring hardware and software a team can reuse rather than rebuild.
Why FPGA
Adaptable Interfaces
Sensors, networks, and robot variants change over a long service life. Reconfigurable I/O absorbs new interfaces and protocols without a new silicon spin, and can consolidate functions that would otherwise need separate parts.
The Challenge
Power, Thermal, and Form-Factor Limits
Perception, networking, and control must fit tight power, thermal, and board-space budgets on battery-powered or sealed platforms.
Why FPGA
Consolidated SoC Integration
One SoC FPGA combines Arm processors, programmable logic, AI acceleration, and I/O, replacing several discrete parts and the power, board area, and heat they bring.
Why Altera
Physical-World Interface
Configurable logic connects and transforms sensor and actuator data across the interfaces a robot actually uses, from camera and depth inputs to encoders, industrial networks, and motor control, subject to the device selected and the design validated.
Reusable Foundation
Teams can begin from a working example rather than an empty project. Altera provides reference designs, IP, and development kits that cover ROS2 control, motor drive with functional safety, and multi-sensor vision.
Architecture Flexibility
The same system concept can be divided differently as sensor counts grow, AI workloads shift, or robot variants diverge, without changing design environment or vendor.
Open Robotics Ecosystem
Partner system-on-modules (SoMs) and boards from Macnica, Trenz, and Terasic let teams build on the tools and suppliers they already use.
From Evaluation to Deployment
Robotics developers need a path from hardware evaluation to repeatable deployment. The Quartus® Prime software is the design environment for that path, used to configure the programmable device and integrate hardware logic, with Nios® V soft processors available as an embedded processor option for control, management, or housekeeping functions where the chosen device and design use it.
Where the selected device and workflow support it, FPGA AI Suite is used to develop an FPGA-based AI accelerator, covering model preparation, compilation, and resource mapping. Framework and device support vary by release, so check the current tool documentation before committing to a model or a target. On the robotics software side, the ROS Consolidated Robot Controller reference design shows how FPGA-side functions connect to a robotics software stack.
Get Started
Reference Designs
ROS Consolidated Robot Controller
Deterministic ROS2-based controller for robotics and AMRs.
Drive-On-Chip with Functional Safety
SIL-ready motor control for safe industrial robotics.
Drive-On-Chip with PLC
Soft PLC implementation for industrial automation.
Holoscan Sensor Bridge (MIPI → 10GbE)
High-speed sensor aggregation for AI perception systems.
4Kp60 Multi-Sensor HDR Camera
High-resolution multi-camera HDR vision system.
4Kp30 Multi-Sensor Camera + AI
Multi-camera platform with onboard AI inference.
HPS TSN 3x2.5G SGMII
TSN-enabled deterministic Ethernet networking example.
Sensor Fusion Platform (PIB)
Multi-sensor integration platform for AMRs and edge gateways.
Smart Camera Demo Kit
Embedded vision demo for industrial robotics and inspection.
IP Portfolio
Production-ready IP for every layer of a robot, from camera and sensor inputs through deterministic networking to motor control and feedback, so teams integrate proven blocks instead of building interfaces from scratch.
Perception & Vision
I/O Sensor Interfaces
Networking Interfaces
Motion Control & Feedback
Common Questions
The stack provides programmable hardware, software flows, IP, reference designs and development kits for sensing, networking and motion functions. Select compatible components for the AMR; the catalog is a development foundation rather than a completed robot configuration.
Timing-critical acquisition and control can run in validated FPGA hardware while ROS 2 manages higher-level orchestration. ROS 2 execution timing depends on the host and middleware configuration, so measure the full path rather than attributing all determinism to the FPGA.
Match the example's exact device, board revision, connectors, memory, motor interface and tool release. Confirm that required daughtercards and licensed IP are available before choosing a kit solely by FPGA family.
Check supported operators, formats, target devices and software releases before integrating the inference IP. Validate model accuracy and estimated resources first, then measure system throughput and latency alongside the robot's sensor and control traffic.
Measure command arrival times, sequence numbers, queue depth and FPGA control deadlines separately. Check host scheduling, networking and interface buffering before modifying an otherwise stable hardware control loop.