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Hiwonder Raspberry Pi 5 Robot Car MentorPi M1 Mecanum-wheel Chassis 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.
- 【Multiple Chassis Configurations】Supports both Mecanum-wheel and Ackermann chassis, allowing flexibility for various applications and meeting diverse user needs.
- 【High-Performance Hardware】Equipped with closed-loop encoder motors, TOF lidar, 3D depth 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.
3D Depth Camera
The 3D depth camera not only enables AI visual functions but also supports advanced features like depth image data processing and 3D visual mapping and navigation.
Raspberry Pi 5 Controller
MentorPi is powered by Raspberry Pi 5 controller allowing you to embark on motion control, machine vision, and OpenCV projects.
STL-19P TOF Lidar
MentorPi is equipped with lidar, which can realize SLAM mapping and navigation, and supports path planning, fixed-point navigation and dynamic obstacle avoidance.
High Performance Encoder Motor
It offers robust force, has a high-precision encoder,and includes a protective end shell to ensure an extended service life.
| Monocular Camera Parameters | |
| Resolution | 30W(640*480) |
| Light-sensitive chip | GC0308(gcoreinc) |
| Camera type | HS-256-650(HS) |
| Aperture | 2.0 |
| Focus | 1.7mm |
| Focal field of view angle | 170° |
| Distortion | -83% |
| Camera interface | USB |
| Size | 30*27*25mm |
| LFD-01 Servo Parameters | |
| Rotation speed | ≤0.11sec/60° 6V |
| Anti-blocking protection | Power cut protection after 5 seconds of stall |
| Rotation range | 0-180 degrees |
| Communication method | PWM pulse-width control |
| Gear type | Plastic shaft |
| Working voltage | 4.8-6V |
| Maximum torque | ≥1.4kg.cm 6V |
| No-load speed | ≤0.11sec/ 60° 6.0V |
| Size | 22.3x12.0x23.2mm |
| Depth Camera Parameters | |||
| Camera model | Nuwa-HP60C | Power supply method | USB |
| Operating range | 0.2-4m | Accuracy | ＜ 2mm@1000mm |
| Depth FOV | H73.8°xV58.8°xD86.4° | Color FOV | H80.9°xV51.7°xD88.9° |
| VBUS | 4.75-5.25V | Supported OS | Windows, Android, Linux |
| Resolution@frame rate (Depth mode) | 640x480@20fps(Max) | Resolution@frame rate (RGB mode) | 1920x1080@20fps(Max) |
| Operating environment | Indoor | Data port | Type C USB 2.0 |
| Size | 89.9x19.0x25.0mm | Power consumption | ＜2W |
| Depth Camera Parameter | |
| Camera model | Nuwa-HP60C |
| Power supply method | USB |
| Operating range | 0.2-4m |
| Accuracy | ＜ 2mm@1000mm |
| Depth FOV | H73.8°xV58.8°xD86.4° |
| Color FOV | H80.9°xV51.7°xD88.9° |
| VBUS | 4.75-5.25V |
| Supported OS | Windows, Android, Linux |
| Resolution@frame rate (Depth mode) | 640x480@20fps(Max) |
| Resolution@frame rate (RGB mode) | 1920x1080@20fps(Max) |
| Operating environment | Indoor |
| Data port | Type C USB 2.0 |
| Size | 89.9x19.0x25.0mm |
| Power consumption | ＜2W |
Wrapped Rear Tail Shell
It can effectively protect the PCB circuit and magnetic ring at the end of the motor from external influences, effectively improving the safety and service life of the motor.
Permanent Magnet Brushed Motor
The permanent magnet DC motor has fast starting response speed, large starting torque and smooth speed change.
High-precision Magnetic Encoder
The permanent magnet DC motor has fast starting response speed, large starting torque and smooth speed change.
Durable Metal Gear
The motor incorporates a full metal gear and metal output shaft, reducing power consumption and extending the motor's service life.
| Size | 212*171*147mm(Depth Camera Version) |
| Weight | 1.2kg |
| Chassis type | Mecanum wheel chassis |
| Motor | 310 metal gear geared motor |
| Encoder | AB-phase high-accuracy quadrature encoder |
| Material | Full metal aluminum alloy chassis, anodizing process |
| ROS controller | RRC Lite controller + Raspberry Pi 5 controller |
| Control method | App, wireless handle and PC control |
| Camera | Angstrong binocluar 3D depth camera |
| Lidar | ldrobot STL-19P |
| Battery | 7.4V 2200mAh 20C LiPo battery |
| OS | Raspberry Pi OS + Ubuntu 22.04 LTS + ROS2 Humble (Docker) |
| Software | iOS/ Android app |
| Communication method | WiFi/ Ethernet |
| Programming language | Python/ C/ C++/ JavaScript |
| Storage | 64GB TF card |
| Item | Specification |
| Model | Mecanum-wheel chassis version(Depth camera) |
| Weight | 1.2kg |
| Size | 212*171*147mm |
| Chassis type | Mecanum wheel chassis |
| Motor | 310 metal gear geared motor |
| Encoder | AB-phase high-accuracy quadrature encoder |
| Material | Full metal aluminum alloy chassis, anodizing process |
| ROS controller | RRC Lite controller + Raspberry Pi 5 controller |
| Control method | App, wireless handle and PC control |
| Camera | Angstrong binocluar 3D depth camera |
| Lidar | ldrobot STL-19P |
| Battery | 7.4V 2200mAh 20C LiPo battery |
| OS | Raspberry Pi OS + Ubuntu 22.04 LTS + ROS2 Humble (Docker) |
| Software | iOS/ Android app |
| Communication method | WiFi/ Ethernet |
| Programming language | Python/ C/ C++/ JavaScript |
| Storage | 64GB TF card |
| Package size | 41*22*18cm |
| Package weight | About 2.1kg |
Questions & Answers
Have a Question?
-
how fast can it run
Hello,
We don't record the exact speed of the MentorPi, but you can refer to the motor speed as follows:
-
Hi, What is the "Hiwonder Raspberry Pi 5 Robot Car MentorPi M1 Mecanum-wheel Chassis ROS2-HUMBLE Support SLAM and Autonomous Driving" used for? I mean, after learning all that which is mentioned in the product page i.e. (Robot Underlying Function, Mapping Navigation Functions, Vision Processing Functions, Human-Machine Interaction Function, Deep Learning Function) - how do I use all that learning for? Are all these learnings used for a job or for a hobby or to start a robotics company? I am totally clueless about the real life use cases of the learnings I get from this product. Can you please guide me on this. Thank you, Vnay
Hello,
This robot is designed for programming education and robot development! For detailed tutorials about this robot, please refer to the following link: https://drive.google.com/drive/folders/1Ox5xN5zpxXqDK-9ruDwwgcQgePXvMvHr?usp=sharing
