Things used in this project
Hardware components
| MentorPi M1 Chassis |
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| Bracket Set |
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| Raspberry Pi 5 |
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| 64 GB SD Card |
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| Cooling Fan |
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| Raspberry Pi Power Supply C |
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| RPC Lite Controller + RPC Data Cable |
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| Battery Cable |
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| Lidar + 4PIN Wire |
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| Lidar Data Cable |
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| 8.4V 2A Charger (DC5.5*2.5 Male Connector) |
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| 3D Depth Camera |
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| Depth Data Cable |
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| Wireless Controller |
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| Controller Receiver |
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| EVA Ball (40mm) |
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| Card Reader |
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| 3PIN Wire (100mm) |
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| WonderEcho Pro AI Voice Interaction Box + Type C Cable |
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| Accessory Bag |
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| User Manual |
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Story
Imagine your MentorPi M1 robot car can already navigate autonomously and avoid obstacles in a room. But what if you want it to truly understand more complex rules, like stopping at a red light or recognizing a stop sign? General-purpose AI models often struggle to accurately identify these specific, sometimes tiny, targets.
This guide will take you through a complete, hands-on project: teaching your robot car to "read" traffic signals. We will use the MentorPi M1 itself to complete the entire workflow—from data collection and model training to deployment and application. The most compelling aspect of this project is that you can build a full closed loop from the physical world to artificial intelligence using essentially this single device.
Part 1: Why is This an Ideal Use Case for the MentorPi M1?
A. The Hardware Closed Loop: Perfect Unity of Data and Application
Typically, developing a custom vision model for a robot is a cumbersome process: you collect data with one device, train the model on another more powerful computer, and then struggle to deploy it back onto the robot. This process is often plagued with compatibility issues.
The MentorPi M1 elegantly solves this pain point. Its built-in 3D depth camera is the perfect tool for capturing high-quality, first-person perspective road images. We can build a simple "mock street" on a desk or floor using printed pictures of traffic lights and road signs. As the car patrols this environment, what it "sees" is the exact scenario it will need to understand and respond to. This high consistency between the data and the application scenario is crucial for the model’s ultimate success.