Product Selection Guide | Choosing the Right ROS AI Robot MentorPi, JetAuto, ROSOrin
Quick Answer
Choose MentorPi: You have a limited budget and want a focused, entry-level ROS2 learning experience. Ideal for basic SLAM navigation, introductory AI vision, and voice interaction. Compact, portable, and a complete low-cost package.
Choose JetAuto: You need stronger perception capabilities and NVIDIA Jetson computing power for complex AI vision, deep learning, and advanced SLAM projects.
Choose ROSOrin: You want a well-rounded hardware and software configuration with modular expansion and the ability to switch between different chassis types. Covers ROS2 learning, algorithm research, and broader development needs.
I. Introduction
ROS mobile robots available on the market are becoming increasingly feature-rich. Many products now support ROS2 development, SLAM mapping and navigation, AI vision, and large language model applications. However, different platforms vary significantly in main controller computing power, perception hardware, and chassis design. Some are best suited for ROS2 beginners and basic experiments, while others can handle complex AI algorithms and advanced intelligent applications. Understanding your learning goals and project requirements is the key to choosing the right robot platform.
II. Product Overview
Hiwonder offers three ROS2 mobile robots — MentorPi, JetAuto, and ROSOrin. All three natively support ROS2 Humble/Iron and include complete examples for SLAM, 3D vision, and multimodal AI interaction. They are clearly differentiated in computing power, perception hardware, chassis structure, and expansion capabilities.
1. MentorPi: Compact and Portable Classic ROS Learning Platform
Core Positioning: Best value-for-money, compact and lightweight. Ideal for entry-level learning and rapid prototype verification.
Hardware Configuration: Powered by a Raspberry Pi 5, equipped with a classic Mecanum-wheel chassis, TOF LiDAR, Aurora 930 Pro depth camera, and WonderEcho Pro AI voice interaction module.
Battery Performance: Features a 7.4V 2200mAh battery. The compact size makes the robot extremely lightweight and easy to carry.
Core Advantages: Fully functional with excellent cost-performance. It covers the complete ROS mobile robot learning pipeline. If you do not have strong requirements for high-end perception performance or complex terrain traversal, MentorPi is the preferred choice for classroom teaching, daily entry-level learning, and small maker projects.
2. JetAuto: Perception-Focused High-Performance ROS Platform
Core Positioning: Strong computing power and top-tier perception configuration. Designed for advanced vision AI and complex SLAM tasks.
Computing Power & Chassis: Supports both Raspberry Pi 5 and the full NVIDIA Jetson series (Nano / Orin Nano / Orin NX). The chassis uses a swing-arm suspension Mecanum-wheel design. All four wheels maintain firm ground contact, delivering strong traction and highly accurate odometry.
Hardware Configuration: The depth camera is upgraded to the Orbbec Astra Pro Plus, which offers higher RGB resolution and frame rate (1080P@30fps) along with a longer depth range. It performs especially well in object detection and face recognition. LiDAR options include the SLAMTEC A1 (stable and balanced) or the higher-performance EAI G4. An optional 7-inch display is available for more convenient debugging and interaction.
Battery Performance: Equipped with an 11.1V 6000mAh large-capacity battery that supports longer development sessions.
Core Advantages: For developers who need to dive deep into vision AI algorithms, complex-scene mapping and navigation, or require higher-level environmental perception, JetAuto provides significantly more hardware performance headroom.
3. ROSOrin: High-Aesthetic Modular ROS2 Robot
Core Positioning: Well-designed structure with multi-chassis switching capability. A modular, all-round ROS2 development platform with strong performance.
Distinctive Design: Compared with the previous two models, ROSOrin offers better exterior enclosure. It supports quick switching among three chassis types — swing-arm suspension Mecanum wheels, Ackermann, and differential drive — allowing developers to test different motion models and navigation algorithms on the same hardware platform.
Hardware Configuration: Also supports Raspberry Pi 5 and the full Jetson series. Comes with TOF LiDAR and the Aurora 930 Pro depth camera. The swing-arm suspension system helps maintain smooth and stable motion.
Battery Performance: Standard 11.1V 6000mAh large-capacity battery.
Core Advantages: One device covers multiple chassis motion models. You can directly compare how different chassis structures affect motion control, path planning, and navigation algorithms. With balanced hardware and high expandability, ROSOrin offers excellent versatility and research value. It is a strong choice for users who want both learning breadth and good cost-performance.
III. Key Differences Explained
1. Aurora 930 Pro vs. Astra Pro Plus Depth Camera
The Aurora 930 Pro is sufficient for most vision teaching scenarios and conventional 3D vision tasks such as grasping.
The Astra Pro Plus features a dedicated depth processing chip. Its RGB camera reaches 2-megapixel resolution and supports 1080P@30fps. The improvement in RGB image quality is more noticeable, making it better suited for complex visual tracking and more demanding point-cloud or depth applications.
In short: If you simply need the ability to work with 3D vision, the Aurora 930 Pro is usually enough. When you require more stable vision quality or algorithms that demand higher-quality perception data, the Astra Pro Plus has a clear advantage.
2. SLAMTEC A1 vs. EAI G4 LiDAR
The SLAMTEC A1 (custom version) is optimized for stability and supports frequency adjustment. It fully meets the needs of SLAM learning and standard navigation applications.
The EAI G4 offers stronger data sampling capability and richer environmental information. It performs better with mapping algorithms such as Gmapping, Hector, and Cartographer, making it more suitable for complex environments and advanced algorithm research.
3. Standard Mecanum Chassis vs. Suspension Mecanum Chassis
A standard Mecanum-wheel chassis supports 360° omnidirectional movement with flexible steering and lateral motion. It is well suited for learning mobile control on flat surfaces.
A suspension Mecanum chassis uses an independent suspension design that keeps all four wheels in better contact with the ground. This improves stability, reduces the impact of uneven surfaces, and enhances passability on more complex terrain.
IV. Core Parameter Comparison
| Core Dimension | MentorPi | JetAuto | ROSOrin |
|---|---|---|---|
| Main Controller | Raspberry Pi 5 | Raspberry Pi 5 / NVIDIA Jetson series | Raspberry Pi 5 / NVIDIA Jetson series |
| Chassis Structure | Classic Mecanum wheels | Swing-arm suspension Mecanum wheels | Modular (Mecanum / Ackermann / Differential) + Swing-arm suspension |
| Vision Perception | Aurora 930 Pro | Astra Pro Plus | Aurora 930 Pro |
| Environment Mapping | TOF LiDAR | SLAMTEC A1 / EAI G4 (optional) | TOF LiDAR |
| Battery | 7.4V 2200mAh | 11.1V 6000mAh | 11.1V 6000mAh |
| Dimensions | 212 × 171 × 147 mm | 302 × 260 × 256 mm | 277 × 212 × 166 mm (Mecanum version) |
| Core Advantages | Compact, lightweight, excellent cost-performance | Top-tier perception, strong AI vision capability | High aesthetics, modular three-in-one chassis, good cost-performance |
| Best For | Limited budget. Focus on completing the full ROS2 basic workflow and conventional mobile robot experiments. Prioritize portability and value. | Emphasis on perception and algorithms. Need higher-spec vision and LiDAR for complex local AI inference, high-precision mapping, or visual tracking. | Want one device that supports multiple chassis types. Value learning breadth, expandability, and balanced performance from entry to advanced levels. |
FAQ
Q1: I only want to learn basic ROS2. Which one should I choose?
A: If your goal is to learn ROS2 fundamentals, SLAM mapping, autonomous navigation, and basic AI vision, MentorPi offers the best cost-performance. If you plan to go deeper into AI algorithms or more comprehensive robot development, consider JetAuto or ROSOrin.
Q2: Can I learn AI vision without an NVIDIA Jetson?
A: Yes. The Raspberry Pi 5 can handle basic 3D vision processing and simple visual tracking tasks. However, if you need to run more complex deep learning models, local AI inference, or larger-scale algorithm deployment, a Jetson platform will give you a clear advantage.
Q3: What are the advantages of the swing-arm suspension Mecanum chassis compared with a standard Mecanum chassis?
A: The swing-arm suspension keeps all four wheels in better contact with the ground. It gives the robot better stability and motion performance on uneven or complex surfaces.
Q4: All three robots support SLAM navigation. What are the practical differences?
A: The main differences lie in the perception hardware and chassis design. MentorPi is well suited for learning the basic SLAM workflow and standard navigation. JetAuto, with higher-spec vision and LiDAR options, performs better in complex environments. ROSOrin allows you to study how different motion models affect navigation by switching between chassis types.
Q5: If I plan to go deeper into robot development later, which one is more suitable?
A: Both JetAuto and ROSOrin are good choices if you want to focus on AI vision, navigation planning, and algorithm development. If you are also interested in comparing different chassis motion models, ROSOrin is the more flexible option.
Q6: What is the practical value of ROSOrin’s multi-chassis switching feature?
A: Different chassis correspond to different motion models. Mecanum wheels support omnidirectional movement, Ackermann more closely resembles real vehicle kinematics, and differential drive is a common structure for mobile robots. Being able to switch between them helps you understand different kinematic principles and how they influence path planning and navigation control.