With the popularity of Robot Operating System (ROS) in education, scientific research and development, more and more ROS robot platforms have emerged to meet the needs of users at different levels. Whether you are a beginner, advanced developer, or professional researcher, choosing a suitable ROS robot is crucial.
This article will introduce 10 kinds of popular ROS educational robots based on NVIDIA Jetson ORIN NANO, NVIDIA Jetson ORIN NX, and Raspberry Pi to help you find the one that suits you best!
1. Yahboom Dofbot robotic arm (Based on NVIDIA Jetson Nano B01)
Suitable for: Intermediate ROS learners, robotic arm enthusiasts
Dofbot is a 6-DOF robotic arm based on Jetson Nano, supporting ROS1/ROS2, suitable for robot grasping, visual recognition and other applications. It has high-precision servo control and OpenCV visual processing capabilities, suitable for learning robotic arm control, computer vision and AI robot development.
Highlights:
6-DOF robotic arm design
Support Python and ROS programming
Compatible with Jetson Nano, can run deep learning algorithms
2. Yahboom MicroROS-Pi5 Robot Car
Suitable for: ROS2 developer, embedded robot enthusiast, Raspberry Pi maker
The MicroROS-Pi5 ROS2 car is developed based on Raspberry Pi 5. It uses the ROS2-HUMBLE development environment and Python3 programming, and uses OpenCV image processing and MediaPipe machine learning algorithms to achieve robot motion control, AI visual interaction, SLAM mapping navigation, RViz simulation, and multi-machine synchronous control.
Highlights:
Lightweight ROS2 (MicroROS) support
Rich sensor interfaces (IMU, ultrasonic, etc.)
Ideal for low-cost entry into ROS2
Suitable for: ROS advanced developers, autonomous driving researchers
RDK X3 is a high-performance ROS robot equipped with Rockchip RK3566 chip, supporting ROS1/ROS2, suitable for SLAM, navigation and AI vision tasks.
Highlights:
Powerful computing power (RK3566 CPU + NPU)
Supports sensors such as LiDAR and depth camera
Suitable for autonomous driving and intelligent robot research
4. Yahboom ROSMASTER R2 with Ackermann structure
Suitable for: ROS beginners, educational institutions
ROSMASTER R2 is a modular ROS car that supports multiple sensor expansions and is suitable for learning ROS basics, SLAM and path planning.
Highlights:
Modular design, easy to expand
Supports LiDAR, IMU, camera
Equipped with detailed ROS tutorials, suitable for novices
Suitable for: Advanced ROS users, multi-machine collaborative research
This upgraded ROS car uses NVIDIA Jetson and Raspberry Pi, which has stronger computing power and supports multi-machine collaboration and advanced AI applications.
Highlights:
Optional NVIDIA Jetson ORIN NANO SUPER/NVIDIA Jetson ORIN NX SUPER/NVIDIA Jetson NANO B01/Raspberry Pi 5, with stronger performance
Supports multi-robot collaborative experiments
Suitable for advanced applications such as SLAM, 3D vision, etc.
6. Yahboom JetCobot 7-axis visual collaborative robotic arm
Suitable for: AI+ROS learners, Jetson Nano users
JetCoBot is an AI car based on NVIDIA Jetson Nano/Orin NANO SUPER/Orin NX SUPER, supporting ROS and deep learning, suitable for computer vision and autonomous navigation learning.
Highlights:
Official JetBot open source project compatible
Supports PyTorch/TensorFlow
Suitable for application development combining AI+ROS
7. Yahboom DogZilla S1/S2 (Robot Dog)
Suitable for: Bionic robot enthusiasts, ROS advanced developers
DogZilla S1 is a quadruped robot that supports ROS control and is suitable for studying gait algorithms and dynamic balance.
Highlights:
12-DOF quadruped structure
Supports ROS1/ROS2
Suitable for robot motion control research
Suitable for: Field robot, rescue robot developers
MUTO RS adopts track design, adapts to complex terrain, supports ROS, and is suitable for research on field navigation and harsh environment applications.
Highlights:
All-terrain track design
Supports lidar and deep vision
Suitable for search and rescue, exploration and other scenarios
Suitable for: ROS creative developers, robot competition players
Transbot SE is a transformable ROS robot that can switch between car and robot arm modes, with various ways to play.
Highlights:
Freely switch between two forms
Support Python and ROS programming
Suitable for creative robot projects
10. Yahboom MicroROS Self-Balancing Robot
Suitable for: Control algorithm researchers, ROS embedded development
This self-balancing robot is based on ESP32 and MicroROS, suitable for studying PID control, IMU sensor fusion and ROS2 embedded development.
Highlights:
Lightweight ROS2 (MicroROS) support
Excellent platform for learning balancing robot control
Low cost, high playability
How to choose the ROS robot that suits you best?
Each robot has its own unique advantages. When choosing, you need to consider your learning goals, budget, and application scenarios. Whether you are getting started with ROS or delving into robot algorithms, there is always one for you!
Which one do you prefer? Feel free to share your choice in the comment section! 🚀