Autonomous mobile robot EDU MentPi LIDAR SLAM ROS2 Humble Mecanum 2D 2-axis camera Raspberry Pi 5 16GB

2DOF Monocular Camera

MentorPi is equipped with a 2DOF monocular camera and two LFD-01 anti-lock servos, allowing for a complete 360° view with no blind spots when combined with its versatile chassis.

Raspberry Pi 5 Controller

The Hiwonder MentorPi robot car is powered by Raspberry Pi 5, allowing you to start with motion control, computer vision, and OpenCV projects.

STL-19P TOF Lidar

Equipped with high-performance Lidar, the MentorPi M1 robot uses SLAM technology for precise mapping and navigation. It can plan complex paths, navigate to greenhouse points, and dynamically avoid obstacles.

High-performance encoder motor

This motor offers powerful and precise movement, thanks to a high-precision encoder. The protective end cap ensures a longer service life.

Dual controller design for efficient collaboration

List of functions 

Lidar function

Mentor Pi is equipped with lidar, which supports path planning, fixed-point navigation, obstacle navigation and avoidance, multi-algorithm mapping, and performs lidar guard and lidar tracking functions.

Lidar mapping and navigation

MentorPi can perform advanced SLAM functions by lidar, including localization, mapping and navigation, path planning, dynamic obstacle avoidance, lidar tracking and protection, etc.

2D Lidar mapping method

TOF Lidar uses the SLAM toolkit for mapping algorithms and supports fixed-point navigation, multipoint navigation, and TEB path planning

Multipoint navigation

MentorPi is equipped with a high-precision Lidar that enables real-time environmental detection. It supports both fixed-point and multipoint navigation, making it suitable for complex navigation scenarios.

Multi-robot cooperation

Mapping and Navigation: By leveraging multi-robot communication and navigation technology, several robots can collaborate to simultaneously map their environment. This enables multi-robot navigation and path planning.

Dynamic obstacle avoidance

By using TOF Lidar, MentorPi can detect obstacles during navigation and intelligently plan its path to avoid them effectively.

Lidar tracking and protection

MentorPI can work with Lidar to scan and subsequently track an approaching moving target. MentorPI uses Time-of-Flight (TOF) Lidar to scan the secured area. Upon detecting an intruder, it will automatically turn towards them and trigger an alarm.

Monocular camera function

Equipped with a 2DOF monocular camera, the robot car offers a 360° field of view thanks to its agile movement.

Color recognition and tracking

Using OpenCV, the MentorPi M1 robot car can track objects of a specific color. Once you select a color in the app, the robot will emit a light of the same color and follow any object with that color.

Target tracking

Hiwonder MentorPi's vision positioning allows it to accurately locate and track a target object.

QR code recognition

The Raspi MentorPi M1 car robot can recognize and decode the content of custom QR codes, then display the information.

Next line of sight

The Raspberry Pi robot can follow a line of a custom color. You simply select the desired color, and the robot will identify and follow the line.

YOLO Object Recognition

The MentorPi robot car uses the YOLO (You Only Look Once) deep learning algorithm and a model library to recognize objects in its environment.

MediaPipe development, improved interaction

Using the MediaPipe framework, the MentorPi robot is capable of advanced functions such as fingertip recognition, human body recognition, and 3D face detection.

Deep learning, autonomous driving

In the ROS system, Mentori deployed the PyTorch deep learning framework, the open source image processing library OpenCV, and the YOLOV5 target detection algorithm to help users who want to explore the field of autonomous driving technology easily take advantage of AI autonomous driving.

Python Programming

The Python code is open source, with detailed annotations to facilitate self-learning.