Physical AI is based on the idea that intelligence shouldn’t stop at perception: instead, it should bring to life systems able to sense their environment, reason about it, and act on it – all in one continuous loop.It’s what makes the difference between a device that observes and one that acts.Autonomous mobile robots are one of the clearest expressions of that idea in the real world.
Imagine an autonomous delivery robot working around a warehouse, carrying items to the correct shelf or placing them on a conveyor belt for inspection or packaging.On its way, it may have to stop as someone steps into its path, or adjust its route if a cart has been moved.It must understand its surroundings, determine its position, calculate a path, and respond as the environment changes, until it reaches the right destination. It must control its wheels precisely, read sensors continuously, and react quickly to nearby obstacles – all at the same time.
In all autonomous mobile robots, or AMRs, these tasks require two different kinds of computing: navigation, computer vision, and AI benefit from the flexibility and performance of Linux; motor control and time-sensitive sensor management require the responsiveness of a microcontroller.VENTUNO Q brings these two worlds together, combining a Linux-capable MPU with a real-time MCU on a single platform.To explore the potential of this architecture, let’s imagine how it could become the central computing platform for a food-delivery AMR, where perception, ROS 2, Edge AI and physical control could work together.
Two computing worlds, one robot In a conventional AMR prototype, high-level computing and motor control are often distributed across separate boards.A Linux computer runs ROS 2 and processes data from cameras and LiDAR, while a microcontroller manages motors, encoders and local sensors.This approach works, but it also creates integration work.
Developers must connect the boards, define their communication protocol, maintain multiple development environments and debug the interaction between systems designed independently from one another.VENTUNO Q provides a more integrated starting point.Its MPU can host Linux, ROS 2, navigation software, computer vision and AI workloads.
Its MCU can remain close to the physical robot, managing wheel control, encoder feedback, inertial measurements, short-range sensing and communication with motor drivers.The separation of responsibilities is clear: the MPU determines where the robot should go, while the MCU turns that decision into controlled physical movement.Together, the two sides allow the system to sense, decide, and act.
Sense: giving the robot an understanding of its environment As the delivery robot travels through a warehouse, hotel, or even a restaurant or public space where it is at work to deliver orders, it must continuously build a picture of what is happening around it.No single sensor can provide all the information it needs, so the architecture combines several complementary sources.A 2D LiDAR can provide the geometric foundation for navigation.
Its laser scans describe walls, corridors, furniture and other structures around the robot.Combined with wheel odometry and inertial data, these scans can be used by SLAM software to build a map and later help the robot estimate its position within it.However, a LiDAR scanning on a single plane cannot describe every obstacle.
A table surface, an open drawer or an object protruding into the robot’s path may sit above or below that plane.A stereo-depth camera can add three-dimensional information, helping the robot perceive shapes and obstacles that may not appear clearly in a planar scan.A MIPI camera can provide visual input for more application-specific tasks, such as detecting people, recognizing a delivery station or checking whether a tray is present. Closer to the robot, an Arduino Modulino Distance node monitors blind spots or detects objects immediately in front of the chassis.
A Modulino Movement provides acceleration and angular-rate data, contributing to motion estimation and helping identify unusual vibration, collisions or unexpected movement.Wheel encoders complete this picture by measuring how the robot is actually moving.The MCU can use their feedback to regulate wheel speed, while the resulting odometry can be shared with the navigation software running on Linux.
Rather than relying on a single “smart” sensor, the robot develops awareness by combining geometric, visual, inertial and mechanical information.Decide: ROS 2 as the software backbone On the MPU, ROS 2 provides a common software environment for connecting all these elements.The LiDAR publishes laser scans.
The camera pipeline provides images or depth information.The robot publishes odometry and inertial measurements.Navigation components consume this data to estimate the robot’s position, plan a route and respond when the environment changes.
During its first exploration of a space, a ROS 2 SLAM package can combine LiDAR data and odometry to create a map.Once that map is available, the robot can localize itself and use Nav2 to move between destinations.If the application sends the robot from the warehouse to the packaging station, Nav2 calculates an appropriate route and continually updates it as new sensor information arrives.
It then generates velocity commands describing how quickly the robot should move forward and rotate.Those commands express the intended movement, but they do not directly control the motors.That responsibility moves from the Linux domain to the MCU.
Act: from a ROS 2 command to wheel movement When Nav2 publishes a velocity command, an MPU-to-MCU communication layer transfers the request to the real-time control application.The MCU translates the requested linear and angular movement into targets for the left and right wheels.It then communicates with the motor controllers – potentially over CAN – and uses encoder feedback to maintain the required wheel speed.
This control loop can run with predictable timing, independently of the larger ROS 2 software graph operating on Linux.Information also travels in the opposite direction.Encoder measurements, inertial data, short-range distance readings and drive-system diagnostics can move from the MCU to the MPU, where they become available to the ROS 2 application.
This connection between the two computing domains is one of the most important elements of the architecture.It allows high-level navigation software to interact with the physical robot without asking Linux to manage every time-sensitive electrical and mechanical detail.It also creates space for local reactions.
If a close-range sensor detects an object immediately in front of the robot, the MCU can place the drive system into a controlled stop state and report the event to the MPU.The navigation layer can then reassess the situation, update the route or notify the application.The result is a robot in which intelligence and responsiveness complement one another.
Adding context with edge AI LiDAR, odometry and Nav2 can already allow the robot to navigate through a mapped environment.AI becomes valuable when the robot needs to understand the meaning of what it perceives.The navigation system may detect an obstacle, for example, but a vision model could determine whether that obstacle is a person, a chair or a delivery cart.
This additional context enables more appropriate behavior.When a person approaches, the robot could slow down, wait or announce that a delivery is arriving.At its destination, a visual model might recognize the delivery zone or check whether someone has removed the tray.
The same platform could also inspect the route for objects left on the floor or use vibration data to identify abnormal mechanical behavior.Edge Impulse offers one possible workflow for developing this kind of application-specific intelligence.Through its ROS 2 integration, an inference model can consume image data and make its results available to other parts of the robotic application.
ROS 2 helps the robot decide how to navigate.Edge AI helps it interpret the context in which that navigation takes place.And with the integrated Edge Impulse platform, you can choose to deploy directly into Arduino App Lab – it just takes one click.
Growing the prototype one capability at a time Although the reference architecture includes navigation, depth sensing and AI, a developer would not need to introduce every capability at once.The first prototype could focus only on the mobile base, with the MCU controlling the motors and reading the wheel encoders.Once basic movement is reliable, the control layer could be connected to ROS 2 on the MPU so the robot can accept standard velocity commands and return odometry data.
Adding LiDAR would introduce mapping, localization and autonomous navigation.Depth sensing and short-range sensors could then improve obstacle awareness.Finally, visual AI could transform the generic mobile platform into a delivery robot capable of recognizing people, destinations and application-specific events.
This progressive approach keeps each development step understandable while preserving a clear path toward a more capable system.VENTUNO Q does not remove the engineering challenges involved in building an AMR.Mechanical design, power management, motor sizing, navigation tuning, sensor placement and safety still require careful work.
Its value is in providing a more coherent place to begin.From perception to motion A food-delivery robot is only one expression of this architecture.The same computing model could support warehouse vehicles, inspection robots, hospital logistics systems, agricultural platforms, and other autonomous machines.
The sensors, drive systems and AI models may change, but the fundamental challenge remains the same: connecting high-level intelligence with responsive physical action.By combining a Linux-capable MPU and a real-time MCU, VENTUNO Q gives developers a platform for exploring that complete chain.ROS 2, navigation, perception and edge AI can operate on the MPU, while the MCU manages the sensors and actuators that connect software decisions to the real world.
From understanding the environment to controlling the wheels, VENTUNO Q offers a unified starting point for prototyping the next generation of intelligent mobile robots.Ready to kickstart your journey in robotics? Get your VENTUNO Q from the Arduino Store today. VENTUNO Q is available through our official distribution partners: DigiKey, Farnell, Mouser, Robu.in, and RS, along with our other authorized distributors and resellers.
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