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Hiwonder MentorPi T1 Raspberry Pi Robot Car – Tank Chassis, ROS2 AI Coding Robot with Large AI model ChatGPT, SLAM and Autonomous Driving
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Hiwonder MentorPi T1 Raspberry Pi Robot Car – Tank Chassis, ROS2 AI Coding Robot with Large AI model ChatGPT, SLAM and Autonomous Driving
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Hiwonder MentorPi T1 Raspberry Pi Robot Car – Tank Chassis, ROS2 AI Coding Robot with Large AI model ChatGPT, SLAM and Autonomous DrivingRead the MentorPi T1 tutorials: https: docs. hiwonder. com projects MentorPi T1 en latest . Product Description Hiwonder MentorPi T1 is a smart raspi car robot powered by Raspberry Pi 5 and supports ROS2. Equipped with tank chassis, high speed closed loop encoder motors, Lidar, a 3D depth camera, and large torque servos, it delivers high performance capabilities. These include SLAM mapping, path planning, vision recognition, and autonomous driving.
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Product Description
Hiwonder MentorPi T1 is a smart raspi car robot powered by Raspberry Pi 5 and supports ROS2. Equipped with tank chassis, high-speed closed-loop encoder motors, Lidar, a 3D depth camera, and large-torque servos, it delivers high-performance capabilities. These include SLAM mapping, path planning, vision recognition, and autonomous driving. With YOLOv11 model training, MentorPi T1 can detect road signs and traffic lights. MentorPi open source robot car also deploys a Multimodal Large AI Model to support more advanced embodied AI applications. To help you unlock its full potential, we offer comprehensive MentorPi T1 tutorials and videos designed to inspire and support your AI creative projects.
Aurora930 Pro depth camera
The 3D depth camera powers the AI visuals and handles advanced tasks like depth image data processing and 3D visual mapping/navigation.
Raspberry Pi 5 Controller
MentorPi robot is powered by Raspberry Pi 5 controller, allowing you to embark on motion control, machine vision, and OpenCV projects.
Oradar MS200 Lidar
MentorPi features integrated Lidar for intelligent SLAM mapping and navigation. It expertly handles path planning, fixed-point navigation, and dynamic obstacle avoidance.
High Performance Encoder Motor
This motor combines robust power with a high-precision encoder, all housed in a protective end shell for extended durability and life.
Dual-Controller Design for Efficient Collaboration
Function List
Large AI Model Meets SLAM Mapping/Navigation
MentorPi chatgpt robot applies a multimodal large model to understand users' voice commands, enabling multi-point navigation. Once it arrives at the designated location, it uses a vision language model to gain a deep understanding of the surrounding objects and events. This approach greatly enhances the robot's intelligence, adaptability, and overall user experience, making it better suited to meet real-world needs.
Semantic Understanding
MentorPi T1 leverages a large language model to accurately interpret and analyze user voice commands, enabling a deeper understanding of natural language intent.
Environmental Perception
Powered by a vision language model, Hiwonder MentorPi can interpret objects in its surroundings and understand the spatial layout of the environment.
Intelligent Navigation
MentorPi continuously sends environmental data to the vision language model for real-time in-depth analysis. It dynamically adjusts its navigation path based on user voice commands, allowing it to autonomously navigate to designated areas and deliver intelligent, adaptive routing.
Scene Understanding
With the support of a vision language model, MentorPi car can deeply interpret the semantic information of its environment, including surrounding objects and events within its field of view.
AI Large Language Models Embodied
MentorPi T1 robot car is equipped with a high-performance AI voice interaction module. Unlike conventional AI systems that operate on unidirectional command-response mechanisms, MentorPi leverages ChatGPT to enable a cognitive transition from semantic understanding to physical execution, significantly enhancing the fluidity and naturalness of human-machine interaction. Combined with machine vision, Hiwonder MentorPi exhibits advanced capabilities in perception, reasoning, and autonomous action—paving the way for more sophisticated embodied AI applications.
Voice Control
With ChatGPT integration, MentorPi chatgpt robot can comprehend spoken commands and carry out corresponding actions, enabling intuitive and seamless voice-controlled interaction.
Color Tracking
MentorPi utilizes vision language model analysis to detect and lock onto any object within its field of view. With the integration of a PID algorithm, it achieves precise and real-time target tracking.
Autonomous Patrolling
Utilizing semantic understanding from a large language model, MentorPi can accurately detect and track lines of various colors in real time while autonomously navigating obstacles, ensuring smooth and efficient patrolling.
Vision Tracking
With the advanced perception capabilities of a vision AI large language models, MentorPi T1 car can intelligently identify and lock onto target objects even in complex environments, allowing it to perform real-time tracking with adaptability and precision.
Color Recognition and Tracking
Working with OpenCV, MentorPi can track specific color. After you select the color on the app, it emits light of corresponding color and moves with the object of that color.
Target Tracking
Through vision positioning of the target object, the target object can be better targeted and tracked.
QR Code Recognition
MentorPi can recognize the content of custom QR codes and display the decoded information.
Vision Line Tracking
MentorPi raspi robot supports custom color selection, and the robot can identify color lines and track them.
Wireless Controller Control
MentorPi supports wireless controller control and can connect to the robot via Bluetooth to control the robot in real time.
App Control
WonderPi app supports Android and iOS. Switch game modes easily and quickly to experience various AI games.
ROS Robot Operating System
Global Popular Robotic Communication Framework
ROS is an open-source meta operating system for robots. It provides some basic services, such as hardware abstraction, low-level device control, implementation of commonly used functionality, message-passing between processes, and package management. And it also offers the tools and library functions needed to obtain, compile, write, and run code across computers. It aims at providing code reuse support for robotics research and development.
| 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 |
| Aurora930 Pro Specifications | |||||
| Module parameter | Size | 76.5 × 20.7 × 21.8 mm | Imaging performance | Depth data format | 16-bit Raw |
| Baseline | 40mm | Depth resolution/Frame rate | 640×400 @12fps (FOV: 74°×51°) | ||
| Interface | USB 2.0 Wafer connector | RGB data format | NV12 | ||
| Depth accuracy | ±8mm @1m | RGB resolution /Frame rate | 640×400 @12fps (FOV: 74°×51°) | ||
| Working distance | 15–300 cm | IR data format | 8-bit Raw | ||
| Operating temperature | -10℃ to 55℃ | IR resolution /Frame rate | 640×400 @12fps (FOV: 74°×51°) | ||
| Operating humidity | 0% to 95% RH (non-condensing) | Firmware capabilities | Firmware upgrade | Supports USB OTA Update | |
| Operating illuminance | 3–80,000 Lux | Hot start delay | <300ms | ||
| Power supply | 5V±10%, 1.5A | System compatibility | OS compatibility | Linux / ARMv8 / ROS / Windows | |
| Power consumption | Average <1.6W | ||||
| Safety rating | Class 1 Laser safety | ||||
| Aurora930 Pro Specifications | ||
| Module parameter | Size | 76.5 × 20.7 × 21.8 mm |
| Baseline | 40mm | |
| Interface | USB 2.0 Wafer connector | |
| Depth accuracy | ±8mm @1m | |
| Working distance | 15–300 cm | |
| Operating temperature | -10℃ to 55℃ | |
| Operating humidity | 0% to 95% RH (non-condensing) | |
| Operating illuminance | 3–80,000 Lux | |
| Power supply | 5V±10%, 1.5A | |
| Power consumption | Average <1.6W | |
| Safety rating | Class 1 Laser safety | |
| Imaging performance | Depth data format | 16-bit Raw |
| Depth resolution/Frame rate | 640×400 @12fps (FOV: 74°×51°) | |
| RGB data format | NV12 | |
| RGB resolution /Frame rate | 640×400 @12fps (FOV: 74°×51°) | |
| IR data format | 8-bit Raw | |
| IR resolution /Frame rate | 640×400 @12fps (FOV: 74°×51°) | |
| Firmware capabilities | Firmware upgrade | Supports USB OTA Update |
| Hot start delay | <300ms | |
| System compatibility | OS compatibility | Linux / ARMv8 / ROS / Windows |
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 motor is equipped with a high-precision magnetic encoder, has strong horsepower, high precision, and strong anti-interference ability.
Durable Metal Gear
The motor incorporates a full metal gear and metal output shaft, reducing power consumption and extending the motor's service life.
Hall Encoder Geared Motor
The Hall speed measurement code disc is a speed measurement module that utilizes Hall sensor encoders. Equipped with a strong magnetic disc, it generates AB phase output pulse signals, enabling the detection of motor rotation direction and speed.
7.4V 2200mAh 10C LiPo Battery
The LiPo battery features high-quality 18650 cells and a built-in protection board that safeguards against damage from overcharging, overcurrent, overdischarge, and short circuits. It provides a long service life, with over 300 charge cycles.
| Chassis type | Tank chassis |
| Size | 27.8*19.5*18.2cm |
| Weight | 1.88kg |
| Motor | Hall encoder DC geared motor |
| Encoder | AB-phase incremental Hall encoder |
| Material | Full metal aluminum alloy chassis, anodizing process |
| ROS controller | RRC Lite controller + Raspberry Pi 5 controller |
| Camera | Aurora930 Pro 3D depth camera |
| Lidar | Oradar MS200 |
| Battery | 7.4V 2200mAh 10C LiPo battery with protection board (Continuous operating time: up to 60 minutes) |
| 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 |
| Servo | LFD-01 anti-stall servo (monocular camera version) |
| Materials | Development tutorials; video tutorials; ROS source code, system image and software |
| Package size & weight | around 3.2kg; 39.7*24.4*22cm |
Product Parameters
Dimensional Diagram
MentorPi T1 Packing List(Monocular Camera Version)
MentorPi T1 Packing List(Depth Camera Version)
MentorPi T1 Advanced Packing List(Depth Camera Version)
Specifications
| Item | Specification |
| Model | MentorPi T1(Depth camera version) |
| Chassis type | Tank chassis |
| Size | 27.8*19.5*18.2cm |
| Weight | 1.88kg |
| Motor | Hall encoder DC geared motor |
| Encoder | AB-phase incremental Hall encoder |
| Material | Full metal aluminum alloy chassis, anodizing process |
| ROS controller | RRC Lite controller + Raspberry Pi 5 controller |
| Camera | Aurora930 Pro 3D depth camera |
| Lidar | Oradar MS200 |
| Battery | 7.4V 2200mAh 10C LiPo battery with protection board (Continuous operating time: up to 60 minutes) |
| 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 |
| Servo | LFD-01 anti-stall servo (monocular camera version) |
| Materials | Development tutorials; video tutorials; ROS source code, system image and software |
| Package size & weight | around 3.2kg; 39.7*24.4*22cm |
Hiwonder MentorPi T1 Raspberry Pi Robot Car – Tank Chassis, ROS2 AI Coding Robot with Large AI model ChatGPT, SLAM and Autonomous Driving
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