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Robotics
- Long service time
- Support graphical programming
- Equipped with plenty of modules
- Use micro:bit + micro:bit Expansion board
- Various control and programming methods
- More than 200 structural parts
- Support micro:bit program
- Provide kids with creative life scenes
- Unified connector and plug-and-play
- More DIY extensions are available to every users
Hiwonder
Hiwonder Raspberry Pi 5 Robot Car MentorPi M1 Mecanum-wheel Chassis 2DOF Monocular Camera ROS2-HUMBLE Support SLAM and Autonomous Driving
- 【Raspberry Pi 5 & ROS2 Robot Car】MentorPi is powered by Raspberry Pi 5, compatible with ROS2, and programmed in Python, making it an ideal platform for AI robot development.
- 【High-Performance Hardware】Equipped with closed-loop encoder motors, TOF lidar, 2DOF monocular camera, high-torque servos, and other advanced components to ensure optimal performance and efficiency.
- 【Advanced AI Capabilities】Supports SLAM mapping, path planning, multi-robot coordination, vision recognition, target tracking, and more, covering a wide range of AI applications.
- 【Autonomous Driving with Deep Learning】Utilizes YOLOv5 model training to enable road sign and traffic light recognition, along with other autonomous driving features, helping users explore and develop autonomous driving technologies.
Hiwonder
Hiwonder Raspberry Pi 5 Robot Car MentorPi A1 Ackerman Chassis 2DOF Monocular Camera ROS2-HUMBLE Support SLAM and Autonomous Driving
- 【Raspberry Pi 5 & ROS2 Robot Car】MentorPi is powered by Raspberry Pi 5, compatible with ROS2, and programmed in Python, making it an ideal platform for AI robot development.
- 【High-Performance Hardware】Equipped with closed-loop encoder motors, TOF lidar, 2DOF monocular camera, high-torque servos, and other advanced components to ensure optimal performance and efficiency.
- 【Advanced AI Capabilities】Supports SLAM mapping, path planning, multi-robot coordination, vision recognition, target tracking, and more, covering a wide range of AI applications.
- 【Autonomous Driving with Deep Learning】Utilizes YOLOv5 model training to enable road sign and traffic light recognition, along with other autonomous driving features, helping users explore and develop autonomous driving technologies.