Over 20 years ago, Will Smith’s I, Robot was released, where intelligent machines walked among humans. The movie accurately portrayed a reality that is now closer than ever. In recent years, autonomous mobile robots have moved from research labs into warehouses, hospitals, and even public spaces. Like in the movie, robots are now able to do tasks like delivery, inspection, or mapping. And this demand for the development of self-navigating robots continues to grow.
At the heart of many of these robots lies NVIDIA Jetson, a family of compact yet powerful edge AI computers that bring real-time perception, planning, and control into a single embedded platform. Combined with the Robot Operating System (ROS), it provides an ideal foundation for robust, scalable autonomous AI development.
This blog will guide you through the hardware and software architecture of a differential-drive autonomous robot built using Jetson Xavier and ROS Noetic, from sensors and control boards to navigation and perception.
What is NVIDIA Jetson?
NVIDIA Jetson is a family of edge AI computing platforms, which means they process data on the device instead of using cloud servers. Jetson platforms are like small computers that are ultra compact and use minimal power. That is why they are ideal for powering autonomous mobile robots, drones, and autonomous machines that need real-time processing and AI inference directly on the device.
These are the modules in the Jetson family:
- Jetson Nano
- Jetson AGX Xavier
- Jetson Orin Series
- Jetson Thor
Advanced models like the Orin Series and Thor can manage multiple sensors at once with real-time decision-making. Every Jetson model has its own GPU, CPU, and memory as part of the package. Moreover, they run on Linux-based systems and smoothly integrate with the rest of NVIDIA’s AI software stack.
Hardware architecture
Let’s first look at the hardware components of assembling a robot with Jetson Xavier.
1. Compute core: NVIDIA Jetson Xavier
The Jetson Xavier acts as the robot’s brain. It provides up to 32 TOPS of AI performance while maintaining low power consumption, perfect for edge robotics.
Its GPU enables real-time image processing and neural network inference, while the onboard CPU handles high-level control, mapping, and communication through ROS.
2. Sensor suite
To perceive and understand its environment, autonomous mobile robots integrate multiple complementary sensors:
- 2D LiDAR: Primary sensor for mapping and obstacle detection. It continuously scans the surroundings to generate precise range data used in SLAM (Simultaneous Localization and Mapping).
- Depth camera: Provides spatial awareness, helping the robot detect obstacles and navigate around them using point clouds and RGB-D data.
- Wheel encoders: Mounted on each drive wheel, encoders measure rotational displacement and provide odometry for accurate localization.
- IMU (via STM32): Tracks orientation and angular velocity, useful for stabilizing motion estimation.
- Sharp IR sensor: Adds redundancy for close-range obstacle detection — ideal for situations where LiDAR data might be limited.
- RGB lights: Serve as visual indicators for robot status and user feedback.
This diverse sensor set allows the robot to achieve reliable perception even in challenging environments.
3. Motor control layer
While Jetson handles high-level planning, motor control, and sensor acquisition are delegated to an STM32 microcontroller.
The STM32 reads encoder counts, controls the motor drivers via PWM signals, and relays sensor data to Jetson using ROS Serial over UART protocol. This architecture keeps timing-critical tasks isolated from the main processor and ensures smooth, low-level control.
4. Power management
Autonomous mobile robots are powered by lithium-ion battery packs connected through DC-DC converters. Power rails are isolated for the Jetson, motors, and sensors to prevent noise and brownouts. A power distribution board manages voltage regulation and safety cutoffs.
Software stack
1. Core framework: ROS Noetic
The system is built on ROS Noetic, the widely used framework for robotic software integration.
ROS provides a modular structure where each functionality, such as sensor reading, localization, mapping, and control runs as a separate node communicating through topics and services.
The main software components include:
- Perception layer: Processes LiDAR and depth camera data for obstacle detection and environment mapping.
- Localization and mapping – Uses gmapping for SLAM and maintains a consistent map of the surroundings.
- Navigation layer: Implements move_base for global and local path planning, allowing the robot to move autonomously from one point to another.
- Control layer: A differential drive controller converts velocity commands (cmd_vel) into motor PWM signals handled by the STM32.
- Visualization and debugging: Tools like RViz and rqt_graph help visualize data from the sensors, map building, and TF frames during development.
2. AI and vision integration
Thanks to Jetson Xavier’s GPU, AI models can run directly on the robot without offloading computation to a server.
This enables:
- Real-time object recognition using TensorRT-optimized deep learning models.
- Person and obstacle detection using the depth camera feed.
- Dynamic re-planning in environments with moving objects.
Such onboard inference ensures the robot reacts immediately to its surroundings, which is a key requirement for true autonomy.
3. Communication and data flow
The communication pipeline between hardware components is structured as follows:
- Jetson ↔ STM32 via UART using ROS Serial for motor commands and encoder feedback.
- Sensor Nodes publish data to the ROS network (/scan, /camera/depth, /odom, /imu/data).
- TF Tree maintains spatial relationships between frames: map → odom → base_link → laser → camera_link.
- Wi-Fi Interface allows remote monitoring, teleoperation, and debugging through SSH or RViz over a network.
This modular architecture ensures that each part of the system can be updated or replaced independently without breaking the overall setup.
Development workflow
Developing autonomous mobile robots that operate reliably in the real world requires a structured workflow:
- Simulation and testing: Using Gazebo to simulate sensors, physics, and navigation behavior before running on real hardware.
- ROS workspace setup: Organizing ROS packages for navigation, perception, and motor control in a single catkin_ws.
- Continuous integration: Building Docker containers for reproducible builds across development machines.
- Field tuning: Adjusting parameters for LiDAR filtering, velocity limits, and costmap inflation to improve navigation performance.
This iterative cycle of simulation, testing, and deployment ensures stability and robustness in diverse environments.
Common challenges
Building an autonomous robot on an embedded AI platform isn’t without its challenges. Some common challenges that may occur in this project include:
- Sensor synchronization: Ensuring timestamps from LiDAR, IMU, and encoders align correctly to maintain localization accuracy.
- Thermal management: Jetson Xavier can heat up under heavy AI workloads; proper cooling design is essential for stable operation.
- Power optimization: Balancing motor load and Jetson compute power to achieve longer runtime without performance loss.
- ROS Node optimization: Efficiently handling large sensor data streams (especially cameras) to prevent topic lag and CPU overload.
Each challenge pushes the system toward a more efficient and production-ready design.
How to choose the right Jetson module
We used the Jetson Xavier module in this guide. But you should choose the NVIDIA Jetson module for your robot depending on what your robot needs to do and how powerful it needs to be.
Based on that, if you’re building a small robot with minimal functions, Jetson Nano is the best for the job. It’s affordable and can handle simple AI tasks like using computer vision for detecting objects or following lines.
For more advanced, heavier work, we recommend using the Jetson Orin series. Orin NX can perform up to 157 trillion operations per second, which enables it to process complex data quickly. For example, if your robot needs to safely maneuver through crowds, it will require that level of processing power to avoid hitting obstacles.
Jetson Thor is ideal for industrial robotic development. It delivers over 2000 trillion operations per second, which makes it ideal for robots that need to think and react in milliseconds. This level of power suits humanoid robots or those used in industries like manufacturing, logistics, or healthcare.
Conclusion
NVIDIA is undoubtedly the most important corporation on the planet right now. While its primary image is that of a chip-making company, it is doing much more important and interesting things.
The Jetson family of platforms is one of those next-gen offerings by NVIDIA that has opened the next chapter of humanoid robotics. Jetson brings together cutting-edge hardware and custom software to create autonomous robots.
Such robots integrate advanced sensor fusion, real-time perception, and autonomous navigation to perform critical support tasks, such as navigating to different areas and transporting samples, all while operating smoothly around people and sensitive equipment.
Xavor has extensive experience in using Jetson modules for top-quality AI performance with the full flexibility of the ROS framework. Our engineering teams build robots using Jetson, TensorRT, and other NVIDIA technologies that can safely and efficiently navigate cluttered environments.
Through thoughtful system integration, from STM32-based motor control to onboard AI inference on the Jetson platform, we ensure our robots combine reliability, intelligence, and safety.
Contact us at [email protected] to book a free consultation session.