Hiwonder MentorPi A1 Review: Raspberry Pi 5 Robot Car with Ackermann Chassis and ROS2
by Gaurav Sarraf
The Hiwonder MentorPi A1 is one of the most capable pre-built robot car platforms available for students, developers, and researchers who want to learn ROS2 and autonomous robotics without building hardware from scratch. Powered by the Raspberry Pi 5 and running ROS2 Humble, it combines an Ackermann steering chassis with TOF LiDAR, closed-loop encoder motors, vision cameras, and a dual-controller architecture into a platform that ships ready to run.
At Think Robotics, it ranked seventh by revenue over the last 90 days, making it the top-selling robotics kit on the store. This review covers the hardware, what the A1 can actually do, who it is for, what users are saying, how it compares to alternatives, and the key things to know before purchasing.
ThinkRobotics is the official and largest distributor of Hiwonder products in India. All MentorPi A1 variants are stocked with local warranty support and fast delivery. Multiple kit configurations are available, with or without the Raspberry Pi 5 included.
What Is the MentorPi A1?
The MentorPi A1 is Hiwonder's Ackermann chassis variant of the MentorPi platform. Hiwonder offers the same platform in two chassis configurations: the A1 uses front-wheel steering in the Ackermann geometry that mirrors a real car, and the M1 uses Mecanum wheels for omnidirectional movement. The A1 is the better choice for students studying autonomous vehicle algorithms, lane following, road sign detection, and path planning, with models that capture real-world car kinematics.
The robot uses a dual-controller architecture. The Raspberry Pi 5 handles AI vision processing, ROS2 communication, and high-level decision-making. Hiwonder's RRC Lite expansion board handles motion control, motor drivers, and sensor data processing. This separation keeps time-sensitive hardware control off the Pi's operating system, which improves response latency for low-level tasks.
Hardware Specifications
Raspberry Pi 5 (4GB or 8GB)RRC Lite Sub-ControllerAckermann Steering ChassisSTL-19P TOF LiDAR 360°Ubuntu 22.04 + ROS2 HumbleYOLOv5 + PyTorch + OpenCVWonderPi iOS/Android AppLiPo Battery Pack
STL-19P D500 TOF, 360° scanning, indoor and outdoor
Camera (Standard)
2DOF monocular, 170° FOV, 640x480
Camera (Advanced)
3D depth camera, 73.8° FOV, 1920x1080, 0.2m to 4m depth
Operating System
Ubuntu 22.04 with ROS2 Humble
Programming
Python (open-source code included)
Connectivity
Wi-Fi hotspot (AP mode), Bluetooth
Mobile App
WonderPi (iOS and Android)
Power
Rechargeable LiPo battery pack (7.4V 2200mAh)
Why Ackermann Chassis?
The Ackermann steering geometry means the front wheels turn at slightly different angles, exactly as they do in a real car. This is the correct motion model for anyone studying autonomous driving, vehicle kinematics, or control algorithms for real vehicles.
The Mecanum wheel M1 variant can move in any direction, including sideways. The A1's Ackermann chassis moves like a car: it has a turning radius, cannot strafe, and its kinematics match those used by autonomous driving researchers. For SLAM research, lane-following experiments, and path planning education, the A1 is the more realistic and appropriate choice.
Core Capabilities
What distinguishes the MentorPi A1 from simpler robot kits is the complete AI stack that runs out of the box. These are the four functional layers that define the platform:
🗺️
SLAM Mapping and Navigation
The STL-19P TOF LiDAR performs 360° scanning and feeds into SLAM Toolbox on ROS2. The robot builds maps in real time, localizes within them, and plans paths using TEB planning. Supports fixed-point and multi-point navigation with live obstacle avoidance. Multi-robot coordination is also supported.
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Autonomous Driving with YOLOv5
PyTorch and OpenCV are preinstalled. Users can train YOLOv5 models to recognize custom road signs, traffic lights, lane markings, and objects. Out of the box, the robot follows lane lines, recognizes road signs, and simulates parking and garage-entry maneuvers using on-device inference only.
👁️
Vision Processing
Both versions support color tracking via OpenCV, QR code recognition, target tracking, line tracking, and MediaPipe-based body, face, and fingertip detection. The depth camera version (upgrade kit) adds RTAB-Map visual SLAM, fusing LiDAR and camera for full-color 3D environment maps.
🎙️
AI Voice and Language Interaction
The advanced kit includes ChatGPT integration for natural language voice commands. A user speaks a command, the robot interprets it using a large language model, and executes multi-point navigation based on intent. The vision language model helps the robot understand what it sees at each destination.
Monocular vs Depth Camera
The kit is available in two camera configurations. For most students and developers focused on LiDAR SLAM and autonomous driving, the monocular version covers all core functions. The depth camera version adds 3D spatial awareness and is worth the upgrade if visual SLAM and 3D mapping are part of the learning goals.
Standard version
Monocular Camera
Type2DOF pan-tilt monocular
FOV170°
Resolution640 x 480
Depth dataNo
3D VSLAM (RTAB-Map)No
Point cloud outputNo
Best forLiDAR SLAM, autonomous driving, line tracking
Advanced version
3D Depth Camera
TypeStructured-light depth camera
FOV73.8°
Resolution1920 x 1080 color
Depth range0.2m to 4m
3D VSLAM (RTAB-Map)Yes fused with LiDAR
Point cloud outputYes (via ROS2 API)
Best for3D mapping, spatial reasoning, depth navigation
Pricing and Kit Variants
Think Robotics carries the Hiwonder MentorPi A1 in multiple variants. The options split along two axes: camera type (monocular or 3D depth) and whether the Raspberry Pi 5 is included. For buyers who already own a Pi 5, the without-Pi variant avoids paying for a board twice. The Pi 5 used in this kit is the same standard board sold separately.
Indian pricing is available directly from Think Robotics. Contact the store for current INR pricing and availability on all configurations.
What Users Are Saying
Feedback from the Hiwonder product page, Amazon listings, CNX Software coverage, and the RobotShop community reflects consistent themes: strong hardware quality, responsive Hiwonder support, and a practical setup note around chassis mode selection on first boot.
I am very happy with my purchase. The shipping was very fast and the customer service staff are very supportive and quick. They fixed a wrong model order for me promptly.
PositiveVerified buyer, Hiwonder product page
Received in excellent condition within four days of ordering. Works very well for experimenting with SLAM and ROS2 without the overhead of building a robot from scratch. Setup was straightforward.
PositiveVerified buyer, Hiwonder product page
We found the MentorPi A1 extremely interesting for our university project. A capable platform for applied robotics research with a full ROS2 integration that works out of the box.
Research UseStudents, Chalmers University of Technology, Sweden
The source code is open and well-documented. You can write custom ROS2 packages, create new nodes, and integrate additional sensors using the same interface as the provided tutorials.
DeveloperAmazon listing Q&A, community response
During assembly, the LiDAR sensor cable was slightly short, requiring the LiDAR PCB to be moved to the upper position. Other than that, no issues with the product.
The robot defaults to Mecanum mode in the software configuration. Ackermann buyers need to switch chassis mode using the tool on the Raspberry Pi desktop before the steering servo responds correctly. One step, but easy to miss on first boot.
First-boot tip for Ackermann buyers: The MentorPi defaults to Mecanum mode in software. Before testing steering, open the chassis configuration tool on the Raspberry Pi desktop and switch it to Ackermann mode. This is a one-step process but is easy to overlook and will cause the steering servo to behave unexpectedly if skipped. Hiwonder's documentation at docs.hiwonder.com covers this in the getting-started guide.
How It Compares
A1 vs
MentorPi M1 (Mecanum)
The MentorPi M1 runs the same software stack and carries the same AI capabilities. The core difference is the chassis. The M1 moves in any direction including sideways, suiting indoor navigation where the robot needs to reposition without turning. The A1 uses Ackermann steering, which models car-like motion. For autonomous driving research and vehicle control algorithm education, the A1 is the better fit.
Choose by use case
A1 vs
SunFounder PiCar-X
The SunFounder PiCar-X is a lower-cost Ackermann chassis robot also based on the Raspberry Pi. It lacks LiDAR, has no closed-loop encoder motors, and does not support ROS2 natively. For basic Python programming and simple computer vision, PiCar-X is an accessible entry point. For SLAM, ROS2 Humble, YOLOv5, or a depth camera, the MentorPi A1 is considerably more capable and justifies the higher price.
A1 for full stack
A1 vs
Waveshare UGV Rover
The Waveshare UGV Rover uses a six-wheel drive system with shock-absorbing tires, making it better for outdoor terrain. It uses ESP32 as the sub-controller and Raspberry Pi 4B or 5 as the main controller. The MentorPi A1's Ackermann geometry and full ROS2 integration with LiDAR SLAM are a stronger fit for indoor autonomous driving research. The UGV Rover suits outdoor exploration and rough terrain robotics.
Different environments
Who Should Buy the A1
🎓
University Students
Studying autonomous driving and robotics at university level. The A1 is well-suited for projects involving ROS2, SLAM, path planning, and YOLOv5 object detection. Multiple university teams are documented using it for coursework and research projects.
💻
ROS2 Developers
Learning ROS2 who want a working robot platform to experiment with without spending weeks on hardware assembly and calibration. The robot ships assembled with a working ROS2 environment and documented tutorials that cover the full navigation stack.
🏫
Educators
Running robotics labs who need a platform covering the full autonomous navigation stack, from sensor data to motion control to AI vision, in a single kit that students can get running in one session.
🔬
Researchers
Prototyping algorithms for path planning, obstacle avoidance, or computer vision who need a mobile platform with a standard ROS2 interface and documented sensor specifications without building custom hardware.
Before You Buy
✅
Full Python source code is open-source and documented. Custom ROS2 packages can be written using the same interface as the provided tutorials.
✅
SD card image is preloaded so the robot boots directly into a working ROS2 environment after assembly. No OS setup required.
✅
LAN mode enables ROS2 dev workflows on a laptop. Once switched from AP to LAN mode, you can run RViz2, RQT, and other tools on the same network in real time.
✅
Hiwonder support is responsive. Users who order without a Pi and need the SD card image report getting it promptly by emailing Hiwonder support directly.
⚠️
Switch to Ackermann mode on first boot. The robot defaults to Mecanum mode in software. This must be changed using the desktop configuration tool before the steering servo responds correctly.
⚠️
Battery runtime is reduced under heavy AI load. Running YOLOv5 simultaneously with active navigation shortens battery life. Plan for regular recharging in long sessions.
⚠️
Assembly takes 1 to 2 hours. The A1 ships partially assembled. Hiwonder provides assembly guides and video tutorials. It is not an out-of-box ready product, but most users complete it in one sitting.
⚠️
Connect to local network for development. Default AP hotspot mode is best for basic use. Switch to LAN mode when running RViz2 or other ROS2 tools from a laptop or desktop.
Verdict
The Most Capable Pre-Built Ackermann ROS2 Robot at Its Price Point
The Hiwonder MentorPi A1 is the most capable pre-built Ackermann robot car available at its price point for ROS2 education and autonomous driving research. The combination of Raspberry Pi 5, TOF LiDAR, closed-loop motor control, YOLOv5 deep learning, and a full ROS2 Humble installation covers the complete autonomous navigation stack without requiring users to source and integrate individual components.
For anyone who wants to go from unboxing to running SLAM and AI vision experiments in a single day, the MentorPi A1 delivers that without compromise. Find it at Think Robotics with multiple kit configurations and local support across India.
The MentorPi A1 arrives partially assembled and requires some assembly to complete, including attaching the camera mount and LiDAR, and connecting the cables. Hiwonder provides detailed assembly guides and video tutorials on their documentation site. Most users complete assembly in one to two hours. The Raspberry Pi SD card image is preloaded, so once assembled and powered on, the robot boots directly into a working ROS2 environment.
The monocular camera version includes a 2DOF pan-tilt camera with a 170-degree field of view for color tracking, line following, and basic computer vision tasks. The depth camera version replaces this with a 3D sensor that outputs depth images, color images, and point clouds, enabling RTAB-Map visual SLAM, 3D environment mapping, and richer spatial awareness. For pure LiDAR-based SLAM and autonomous driving, the monocular version is sufficient. For 3D mapping and depth-based navigation, the upgrade is worth it.
Yes. Once the robot is switched from AP mode to LAN mode and connected to your local Wi-Fi network, it appears as a standard ROS2 node. You can run RViz2, RQT, and other ROS2 tools on your laptop on the same network and visualize LiDAR scans, navigation maps, and camera feeds in real time. This is the standard setup for ROS2 development with mobile robots.
Yes. Hiwonder provides the full open-source Python code with documentation and annotations. You can write custom ROS2 packages, create new nodes, modify existing behavior, and integrate additional sensors or peripherals. The robot's RRC Lite expansion board exposes standard ROS2 topics for motor control and sensor data, so custom code integrates using the same interface as the provided tutorials.
The system runs from a LiPo battery pack that powers both the Raspberry Pi 5 and the motor driver. Under heavy AI inference loads, such as YOLOv5 running on the Pi while active navigation is underway, battery life is shorter than during lighter tasks. Hiwonder recommends keeping the battery charged before intensive sessions. The Raspberry Pi 5 in this setup does not require an official 27W USB-C supply, as it is powered by the robot's internal power distribution board.
Get the MentorPi A1 from India's Official Hiwonder Distributor
Multiple kit configurations in stock. Local warranty support and fast delivery across India. Choose with or without Raspberry Pi 5, monocular or depth camera.
Please note that although my status in your website shows fulfilled, my components haven't arrived yet. I am extremely disappointed by the poor coordination between your team and the delivery team.
