So, you've decided to build your first ROS2 robot in 2026. Congratulations! Now, you face the inevitable first and classic decision: which computing brain to choose? The AI-focused NVIDIA Jetson Nano, or the versatile, ecosystem-rich Raspberry Pi 5?
This choice is about much more than comparing processor specs. It's a fundamental decision that steers your learning path and project direction. The Jetson Nano represents the path of dedicated AI acceleration, built for complex neural networks. The Raspberry Pi 5 is the embodiment of general-purpose computing and creation, famed for its ease of use and vast community.
For the vast majority of beginners with a clear goal—to systematically learn core ROS2 concepts and build a functional, intelligent mobile robot (like a self-driving car)—we argue that a well-designed complete robotics kit based on the Raspberry Pi 5 offers a more efficient and less frustrating starting point. This article will clarify the reasoning and introduce a benchmark solution that fully unleashes the Pi 5's potential.
To choose wisely, understand their core differences.
Jetson Nano: The "Edge AI Inference" Accelerator
The Jetson Nano's strength lies in its GPU parallel computing power and mature CUDA ecosystem. It excels at running pre-trained, complex vision models (like various YOLO versions) for real-time object detection on video streams. However, its nature is a "compute module." This means you are responsible for the carrier board, stable power delivery, active cooling, and the integration of every peripheral—LiDAR, motor drivers, IMU. This process is full of engineering hurdles that can easily trap a beginner in hardware debugging hell, diverting you from the main goal of learning ROS2 and robotics algorithms.
Raspberry Pi 5: The "Create Anything" Generalist
The Raspberry Pi 5 takes a different path. It boasts a powerful CPU and a proven, incredibly friendly hardware ecosystem. Its rich GPIO, standardized CSI/DSI interfaces, vast list of compatible add-ons, and the planet's largest maker community are its core advantages. For running ROS2, processing LiDAR data, executing the SLAM and Navigation2 stacks, and performing precise motor control—tasks at the heart of a mobile robot—the Pi 5's capabilities are more than sufficient.
What does an ROS2 beginner truly need?A typical autonomous mobile robot project follows this core chain: perceiving the environment via LiDAR/camera (SLAM mapping) → real-time localization and path planning within the map (Navigation2) → driving the chassis precisely. Visual AI, while a powerful enhancement, is often one part of this larger picture.
For this workflow, system real-time performance, stable multi-sensor drivers, smooth ROS2 node communication, and access to quick community help are far more critical for success and morale than squeezing out a few extra frames per second on a single vision model. Therefore, the Raspberry Pi 5 often provides a smoother launchpad for beginners due to its overall engineering ease, reliable real-time control, and unmatched resource abundance.
The Winning Strategy: Why "Raspberry Pi 5 + Professional Kit" is the Smart ChoiceChoosing the "Pi 5 path" isn't an automatic win. Buying just the board leaves you with a mountain of challenges: driving different motor protocols, providing clean and ample power for all components, designing a rigid chassis, and writing low-level drivers to unify everything under ROS2.
Thus, the more mature question in 2026 isn't "Pi 5 or Nano?" but "What kind of complete Pi 5-based solution should I choose?" The ideal solution maximizes the Pi 5's ecosystem benefits while expertly shielding you from all underlying hardware complexity through pre-integration.
This is precisely the problem solved by fully-integrated ROS2 robot kits like the Hiwonder MentorPi M1. It's not another item to ponder over; it's the "highway" paved for explorers who decide to start their journey with the Pi 5 at the core.
🔥Check your MentorPi Tutorials here or follow Hiwonder GitHub for more repositories.Redefining the "Pi 5 Robot": A Closer Look at the MentorPi M1
The MentorPi M1 has a clear identity: it's not a simple development board but an "out-of-the-box robotics development platform with the Raspberry Pi 5 as its computational heart." It tackles the traditional Pi 5 project pains head-on:
1. Deep Hardware Integration: Beyond the DIY Hassle
It provides a performant Mecanum wheel or Ackermann steering chassis out of the box, paired with closed-loop geared encoder motors for precise, measurable movement.
The sensor suite is complete: a TOF LiDAR for 2D SLAM mapping and obstacle avoidance, and a 3D depth camera for visual recognition and richer environmental perception.
All power and communication cabling between components is pre-optimized and managed. What you receive is a structurally sound, electrically reliable, and sensibly arranged robot, not a box of disparate parts.
2. Pre-Baked Software Stack: Learn in Minutes, Not Weeks
Its killer feature is the pre-configured system image. A single SD card contains a hardware-optimized Ubuntu, ROS2 Humble, and all necessary sensor drivers, robot URDF model, chassis control packages, and example programs.
This bypasses the painful, protracted process of system configuration, driver compilation, and parameter tuning. Power it on, and you can immediately start learning the core concepts: launching the LiDAR node and visualizing data in RVIZ, starting SLAM mapping with a ros2 launch command, or calling Navigation2 services to command autonomous navigation.
3. Structured Learning, Aligned with Core ROS2 Concepts
The accompanying tutorials guide you step-by-step from low-level robot control (topic pub/sub) to SLAM mapping, autonomous navigation (Navigation2), and then to advanced topics like YOLO visual recognition and ChatGPT voice interaction.
This allows you to learn entirely on a real, physically responsive robot. Every ROS2 concept—nodes, topics, services, actions—yields an immediate, tangible result, creating a powerful positive feedback loop for learning.
The core value is this: Through professional integration, it transforms the Raspberry Pi 5 from a "general-purpose compute module" into a "dedicated robot intelligence hub." You can invest 100% of your precious attention and time into learning and experimenting with ROS2 and robotics algorithms themselves, not into endless low-level debugging.
Your 2026 Roadmap: Final RecommendationsSo, faced with the classic Jetson Nano vs. Raspberry Pi 5 question, our advice for 2026 is clearer than ever:
- For most robotics/autonomous driving beginners: If your goal is to master the complete robotics pipeline from perception and planning to control, confidently choose a fully-integrated kit based on the Raspberry Pi 5, like the Hiwonder MentorPi M1. It is the fastest route from the "hardware quagmire" to the "algorithm playground, " ensuring your passion and creativity are focused where they matter most, building a holistic, intuitive understanding of ROS2 robotics development.
- For those focused on edge AI model deployment & optimization: If your primary interest is pushing hardware limits to deploy and optimize the latest vision transformers or dense detection models, then the Jetson series (or its successors) remains your specialized tool of choice. This path typically assumes considerable existing software and systems integration expertise.
In summary, the wisdom of choosing an entry point in 2026 has evolved from comparing single-chip specs to evaluating the maturity and learning efficiency of an entire solution. For anyone eager to step into the world of ROS2 robotics, a "turnkey" platform like the MentorPi M1—built on a powerful general-purpose core yet solving all engineering challenges—is arguably the most reliable bridge across the chasm between a great idea and a working, learning robot.
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