1.8 KiB
Jury Q&A
1. Why 2D, Not 3D?
2D is enough for MVP validation: it shows geometry, timing, sensor positions, routes and state transitions without spending effort on heavy rendering.
2. Where Is Computer Vision?
The MVP uses pseudo-CV: bbox, dimensions, confidence and latency are derived from mock items. The architecture keeps CV output separate from classification, so a real CV service can replace it later.
3. How Is Classification Correctness Proven?
The decision tree shows PASS/FAIL for dimensions and roundness, actual values, thresholds and final category. Tests cover key boundary cases.
4. How Are Dimensions And Circular Section Handled?
Dimensions are checked first against min/max width, depth and height. If they pass, roundness is checked against threshold 0.8.
5. Why Does C Have Priority Over D?
Oversized or undersized items are operationally unsafe for the main line and must be diverted first. Therefore dimensions check precedes roundness.
6. How Is Synchronization Shown?
Cycle timeline shows state order, simulated timestamps, durations and status: done, active, pending or skipped.
7. How Does The Actuator Part Work?
The stop-gate fixes the item. B opens the gate, C extends pusher C, D extends pusher D, then mechanisms return home.
8. What Happens On Jam?
The system enters FAULT, conveyor target speed becomes 0, actual speed decays toward 0, and Reset is required.
9. What Happens On Low CV Confidence?
A warning is logged, but the system still classifies by deterministic dimensions and roundness rules.
10. How To Scale This To A Real Hardware-Software System?
Replace pseudo-CV with a CV service, connect PLC/robot telemetry via backend/WebSocket, persist event logs, calibrate sensor latencies and add recovery policies.