A small conveyor sorter with camera classification, servo diverters, bins and a calibration workflow that works before full AI.
Object sorting is a good robotics project because the world is constrained: objects arrive on a belt, lighting can be fixed and bins are known. That makes it much easier than asking a mobile robot to recognize everything in a room.
Start with color or marker-based sorting before training a model. Once the mechanics work, add a camera classifier. The biggest reliability upgrade is not the neural net; it is consistent lighting and an object spacing gate so the camera sees one item at a time.
Moves objects at predictable speed
Captures images and runs simple classification
Consistent lighting for reliable vision
Pushes objects into bins
Timing for conveyor and servos
Physical outputs for each class
Use HSV thresholds and colored blocks before training models.
Sort known clean objects like cans, caps and cardboard cards.
Count screws, washers or printed parts into bins using the same conveyor.
Treat the generated output as a prototype plan, not a certified product. Body-adjacent, high-voltage, optical-energy and mobility builds need qualified review before real-world use.
No. Start with color, shape or fiducial markers. Add ML after the conveyor is reliable.
Bad lighting, overlapping objects and motion blur cause most errors.
Slow enough for the camera exposure and servo gate. Reliability beats speed in the first version.
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Start with this prompt prefilled, then let RoboHub generate the live parts list, wiring plan, CAD and firmware.
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