Hiwonder Raspberry Pi 5 Robot Car MentorPi A1 Ackermann Chassis ROS2 Supports SLAM and Autonomous Driving (Monocular Camera/Raspberry Pi 5 Not Included)

Using Raspberry Pi 5 driver, compatible with ROS2, and programmed in Python, it is an ideal platform for AI robot development

Supports Mecanum wheels and Ackermann chassis, flexible and adaptable to various applications, meeting different user needs

Equipped with advanced components such as closed-loop encoding motor, TOF LiDAR, 3D depth camera, high torque servo, etc., to ensure optimal performance

Support SLAM mapping, path planning, multi robot collaboration, visual recognition, target tracking, etc

Using YOLOv5 model to train and implement autonomous driving functions such as road sign and traffic light recognition, helping users develop autonomous driving technology

product description

MentorPi is an intelligent robot car equipped with Raspberry Pi 5 and supporting ROS2. It offers two chassis options: Mecanum wheels and Ackermann wheels. It is equipped with a high-speed closed-loop encoder motor, LiDAR, 3D depth camera, and high torque servo, providing high-performance functionality. These features include SLAM mapping, path planning, visual recognition, and autonomous driving. Through YOLOv5 model training, MentorPi can detect road signs and traffic signals. Hiwonder also provides detailed ROS2 tutorials and videos to help users quickly get started. MentorPi is an excellent choice for advanced AI robots.

① 3D depth camera

The 3D depth camera not only realizes AI visual functions, but also supports advanced functions such as depth image data processing, 3D visual mapping, and navigation.

② Raspberry Pi 5 Controller

MentorPi is supported by the Raspberry Pi 5 controller, allowing you to tackle motion control, machine vision, and OpenCV projects.

③ STL-19P TOF LiDAR

MentorPi is equipped with LiDAR, which enables SLAM mapping and navigation, supporting path planning, fixed-point navigation, and dynamic obstacle avoidance.

④ High performance encoder motor

It provides strong force, has a high-precision encoder, and includes a protective end shell to ensure extended service life.

1) Dual control design, efficient collaboration

① Main controller

-ROS controllers (JETSON, Raspberry Pi, etc.)

-AI visual image processing

-Deep neural network

-Human computer voice interaction

-Advanced artificial intelligence algorithms

-Simultaneous Localization and Map Building (SLAM)

② Sub controller

-ROS expansion board

-High frequency PID control

-Motor closed-loop control

-Servo control and feedback

-IMU data acquisition

-Power status monitoring

2) Lidar function

Mentor Pi is equipped with LiDAR, which supports path planning, fixed-point navigation, navigation obstacle avoidance, multiple algorithm mapping, and implements LiDAR guarding and LiDAR tracking functions.

① Lidar mapping and navigation

MentorPi can achieve advanced SLAM functions through LiDAR, including positioning, mapping and navigation, path planning, dynamic obstacle avoidance, LiDAR tracking and guarding, etc.

② 2D LiDAR Mapping Method

TOF LiDAR uses SLAM toolbox for mapping algorithm, supports fixed-point navigation, multi-point navigation, and TEB path planning.

③ Multi point navigation

MentorPi is equipped with high-precision LiDAR, providing real-time environmental detection, supporting fixed-point navigation and multi-point navigation, suitable for complex navigation scenarios.

④ Multi robot collaborative surveying and navigation

By utilizing multiple communication and navigation technologies, multiple robots can collaborate to simultaneously draw maps of their surrounding environment. This can enable multi robot navigation and path planning.

⑤ Dynamic obstacle avoidance

By utilizing TOF LiDAR, MentorPi can detect obstacles during navigation and intelligently plan paths to effectively avoid them.

⑥ Lidar tracking and guarding

MentorPi can be used in conjunction with LiDAR to scan and track moving targets ahead. MentorPi uses TOF LiDAR to scan safe areas. Once an intruder is detected, it will automatically turn towards the intruder and trigger an alarm.

3) 3D depth camera function

Mentor Pi is equipped with Angstrong depth cameras, which can effectively perceive environmental changes and achieve intelligent interaction between AI and humans.

① Color recognition and tracking

MentorPi combined with OpenCV can track specific colors. After selecting a color on the app, it will emit light of the corresponding color and follow the movement of objects of that color.

② Target tracking

By visually locating the target object, it is possible to better aim and track the target object.

③ QR code recognition

MentorPi can recognize the content of custom QR codes and display decoded information.

④ Eye tracking

MentorPi supports custom color selection, and robots can recognize color lines and track them.

⑤ RTAB-VSLAM 3D Visual Mapping and Navigation

By utilizing the RTAB SLAM algorithm and integrating visual and LiDAR data to create a 3D color map, MentorPi can navigate and avoid obstacles in this 3D environment. It also supports global relocation.

⑥ Depth map data, point cloud

Through the corresponding API, MentorPi can obtain depth maps, color images, and point clouds of the camera.

4) Deep learning, autonomous driving

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

① Road sign detection

MentorPi achieves autonomous driving functionality with Al vision by training a deep learning model library.

② Lane keeping

MentorPi is able to recognize the lanes on both sides and maintain a safe distance from them.

③ Automatic parking

Combining deep learning algorithms to simulate real scenes and achieve side parking and storage.

④ Turn to Decision Making

Based on lanes, road signs, and traffic signals, MentorPi will assess traffic conditions and decide whether to turn.

⑤ YOLO object recognition

Utilize YOLO network algorithm and deep learning model library for object recognition.

⑥ MediaPipe construction, AI interaction upgrade

MentorPi utilizes the MediaPipe development framework to implement functions such as fingertip recognition, human body recognition, 3D detection, and 3D face detection.

5) Open source Python programming

MentorPi supports Python programming, and all AI intelligent Python code is open source with detailed comments for easy self-learning.

6) Wireless controller

MentorPi supports wireless controller control and can connect to robots through Bluetooth for real-time control.

7) Application Control

The WonderPi application supports both Android and iOS. Easily and quickly switch game modes to experience various AI games.