Building Smarter AMRs with the Arduino VENTUNO Q Board

Physical AI is about closing the loop from perception to action. An autonomous mobile robot (AMR) in a warehouse or hotel must sense its surroundings, localize, plan a path, and precisely control its wheels – all at once.

That requires two computing worlds:

• Linux for ROS 2, SLAM, Nav2, computer vision, Edge AI
• A microcontroller for motor control, encoders, IMU, short‑range sensors

The Arduino® VENTUNO™ Q brings these together on a single board: a Linux‑capable MPU plus a real‑time MCU, designed as a central brain for AMRs such as food‑delivery robots.

#Two worlds, one robot

In classic AMR prototypes, a separate Linux SBC runs ROS 2 and vision, while another board handles motors and low‑level I/O. That works, but adds wiring, custom protocols, multiple toolchains and painful debugging.

With VENTUNO Q:

• The MPU runs Linux, ROS 2, navigation, vision and AI workloads.
• The MCU stays close to the hardware: wheel control, encoder feedback, inertial sensing, short‑range distance sensors and motor drivers.

The split is clean: the MPU decides where to go, the MCU turns that into motion.

#Sense → Decide → Act

A typical sensor stack might include:

• 2D LiDAR for geometric mapping and navigation
• Stereo‑depth camera for 3D obstacles above/below the LiDAR plane
• MIPI camera for people, stations and tray detection
• Arduino Modulino™ Distance for blind spots and near‑field safety
• Modulino Movement for acceleration and angular‑rate data
• Wheel encoders for odometry and closed‑loop speed control

ROS 2 on the MPU acts as the backbone: LiDAR publishes scans, cameras publish images or depth, the base publishes odometry/IMU. A SLAM package builds a map; Nav2 plans paths and outputs velocity commands.

Those commands are sent via an MPU–MCU link to the real‑time control loop. The MCU converts linear/angular velocities into left/right wheel targets, talks to motor controllers (e.g. over CAN), and uses encoder feedback to maintain speed – independent of Linux jitter. It also streams back odometry, IMU and diagnostics.

Local safety logic can live on the MCU: if a close‑range sensor trips, it can bring the drive to a controlled stop and notify the MPU, which then replans.

#Adding Edge AI context

LiDAR + Nav2 can move the robot; Edge AI helps it understand what it sees. A vision model can classify an obstacle as a person, chair or cart, recognize delivery zones, confirm tray pickup or flag unusual vibration patterns.

With Edge Impulse and its ROS 2 integration, inference nodes can subscribe to image topics and publish results back into the ROS graph. Models can be deployed directly to Arduino App Lab with a single click.

#Grow the prototype step by step

You don’t need everything on day one. A practical roadmap:

1. Start with the mobile base: MCU + motors + encoders.
2. Hook it to ROS 2 on the MPU for cmd_vel and odometry.
3. Add LiDAR for mapping, localization and autonomous navigation.
4. Add depth and short‑range sensing to improve obstacle handling.
5. Add visual AI to turn a generic base into a task‑aware delivery robot.

VENTUNO Q doesn’t remove mechanical or safety challenges, but it gives you a coherent Linux+MCU platform to explore the full chain from perception to wheel motion for your next AMR project.

Source: Arduino Blog