Modern Autonomous Robots Bring Sensing, Computing, and Movement Together

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Published June 30, 2026

Robots are taking on roles with increasing levels of autonomy as their use proliferates in industrial, warehousing, and manufacturing applications. Consequently, designing these robotics systems requires expertise across multiple domains, from machine vision and sensing to embedded computing and motor control. As autonomy increases, so does the need for systems that can interpret and respond to real-time conditions with speed and precision.

At their core, autonomous robots are full-stack systems made up of tightly integrated subsystems that work together seamlessly. To achieve optimal performance, hardware and software components must continuously exchange data, coordinate actions across all subsystems, and act on real-time information. To simplify this complexity, engineers often rely on a layered solutions stack that brings together hardware, software, and intellectual property (IP) blocks to streamline development of autonomous robots, drones, or industrial automation systems.

An effective solutions stack can ensure that data flows freely throughout the three core layers of autonomous robot designs: sensing via the perception layer, computing in the intelligence layer, and movement through the control layer. The three core layers, in turn, leverage cross-layer functions such as power management, communications, the human-machine interface (HMI), functional safety, and cybersecurity. This blog examines how autonomous robots integrate sensing, computing, and motion control through a layered solutions stack, and how this approach helps engineers manage complexity while enabling real-time, coordinated operation.

Building Situational Awareness Through Sensors

When it comes to autonomy, the sensing layer enables a robot to see the world it inhabits and build a model of its work environment. A robot performs simultaneous localization and mapping (SLAM) to build a map of its surroundings and identify its location within the mapped space.

Robots can employ a variety of sensing tools, each with advantages and disadvantages. Vision sensors, or cameras, for example, can capture high-resolution images and enable the robot to perform object recognition. However, adverse lighting and weather conditions can degrade vision sensors’ performance. Light detection and ranging (LiDAR) enables detailed 3D mapping but can be expensive and, like vision systems, can be considerably affected by weather conditions. Radar is often more robust than vision and LiDAR in poor weather or low-visibility conditions. Additionally, radar can measure a target’s distance and relative velocity using time-of-flight (ToF) and Doppler calculations, but it typically offers lower resolution than LiDAR and vision sensors. Ultrasonic sensors offer a cost-effective approach to collision avoidance, yet their range is limited.

Robots can also include inertial measurement units (IMUs) to augment other sensor modalities and measure angular velocity. In addition, a robot may include a Global Positioning System (GPS) receiver to augment position awareness, especially in large-scale outdoor applications such as agriculture.

An advanced autonomous robot will often employ sensor fusion, relying on the outputs of various sensor types to optimize accuracy and reliability. However, sensor fusion also places more demand on the robot’s computing section to process the various sensor inputs and make appropriate decisions.

Turning Sensor Data into Decisions

Data from the sensor layer flows to the computing layer, which performs planning and task selection, navigation and obstacle avoidance, and learning and adaptation based on that sensor data. A microcontroller unit (MCU) can be applied at this layer, and in fact, autonomous robot systems often use a mix of MCUs and other computing hardware for processing, depending on latency, functional safety, workload type, and cost. Traditional MCUs perform sequential computations and may not provide sufficient speed for handling safety-critical decisions in applications with sensor fusion.

In advanced robotics applications with high sensor bandwidth, strict timing requirements, or deterministic multi-stream processing, a field-programmable gate array (FPGA) can complement or, in some cases, replace a traditional MCU-based approach. FPGAs, whose hardware is programmable after shipment, can simultaneously process multiple sensor inputs while also controlling multiple axes of movement. To simplify a design task and speed time-to-market, engineers can choose an FPGA family with a robust portfolio of tics and vision designs, engineers often look for FPGA platforms with support for camera and sensor interfaces, such as Mobile Industry Processor Interface (MIPI) D-PHY/CSI-2, GigE Vision, USB3 Vision, Camera Link, or CoaXPress. Engineers might also opt for industrial and embedded interfaces, such as inter-integrated circuit (I2C), universal asynchronous receiver/transmitter (UART), analog-to-digital and digital-to-analog conversion (ADC/DAC), low-voltage differential signaling (LVDS), Ethernet, EtherCAT, and controller area network (CAN). The exact mix of native IP, partner IP, and reference-design support varies by vendor and platform.

Finally, an FPGA vendor may offer a variety of reference designs that can help engineers get started on robotics projects, while also offering development software and boards (Figure 1) that assist engineers with designing and evaluating their projects before implementing their own custom hardware.

Figure 1: A development board can help engineers start an FPGA design before building their own hardware. (Source: Altera)

Converting Decisions into Physical Action

The movement layer takes action based on decisions made in the computing layer, converting electrical power into movement. Based on instructions from the computing layer, this layer controls the application of power to actuators such as drive wheels, propellers, or the motors that rotate joints or extend limbs.

In addition to actuators, the movement layer includes the power-conversion stages that convert an autonomous robot’s battery power into the precise voltage and current profiles required for each actuator to perform its programmed task, whether that be traversing a factory floor or grasping a workpiece and relocating it. The entire movement layer must be optimized for efficiency, particularly for battery-powered robots, and it must be optimized for safety, with interrupt capabilities to avoid collisions.

The movement layer can include its own sensors—including IMUs for stability feedback, encoders for tracking actuator rotations, and force transducers for verifying proper grip strength. The compute layer uses all this feedback to close the control loop, continuously regulating each actuator and fine-tuning its commands to correct any discrepancies.

Using an FPGA in the compute layer can facilitate movement-layer design. The FPGA can simulate a motor and motor drive, allowing engineers to evaluate their designs before building physical power stages. This drive-on-chip capability supports fine-tuning designs for compliance with functional-safety requirements defined by the Safety Integrity Levels (SILs) specified in the International Electrotechnical Commission (IEC) 61508 standard for the functional safety of electrical, electronic, and programmable electronic systems.^[1]

Accelerating Development with a Robotics Solutions Stack

To facilitate the design of all robotic layers, Altera offers a Robotics Solutions Stack. The stack supports key applications ranging from industrial communications to motor control (Figure 2).

Figure 2: The Altera Robotics Solutions Stack extends from application support to development tools. (Source: Mouser Electronics)

The stack offers reference designs, including a Robot Operating System (ROS) consolidated robot controller and a motor drive-on-chip implementation that helps investigate functional-safety requirements in accordance with the IEC 61508 SIL2 standard. The stack also includes an IP ecosystem that supports a variety of functions, including network and vision interfaces and quadrature encoders. To help get projects started, Altera offers a suite of software development tools as well as development kits and boards in formats including FPGA Mezzanine Card (FMC) and Peripheral Component Interconnect Express (PCIe).

A complete solutions stack is important for building advanced autonomous robots, drones, or industrial automation systems. With Altera’s Robotics Solutions Stack, engineers are equipped with reference designs, IP, and development support for applications ranging from image processing to motor control.

Conclusion

Designing autonomous robots depends on tightly integrating sensing, computing, and actuation into a cohesive, layered system capable of responding to real-time conditions with speed and precision. Situational awareness encompasses diverse sensor inputs and sensor fusion, how meaningful decisions are formed through high-performance compute platforms such as FPGAs, and how those decisions are translated into precise, efficient physical actions through the control and movement layer.

Equally important is the role of a unified robotics solutions stack in reducing design complexity and accelerating development. A well-structured stack enables engineers to move more quickly from concept to deployment while maintaining performance and functional safety requirements.

[1]

 https://assets.iec.ch/public/acos/IEC%2061508%20&%20Functional%20Safety-2022.pdf?2023040501