Architecture

Module Map

src/data/items.ts
src/data/scenarios.ts
        |
        v
src/domain/classifier.ts  ->  src/domain/simulation.ts  ->  React UI components
        |                         |
        |                         +-> metrics, PID, sensors, event log
        v
classification result

Data Flow

Scenario selects a sequence of mock items.
Simulation feeds one item at a time into zone A.
Pseudo-CV and sensors derive measurements from item data.
classifyItem applies deterministic rules.
State machine commands gate and pushers.
UI renders SVG scene, panels, metrics, timeline and event log.

Pseudo-CV

The MVP does not run real ML. Camera output is generated from item dimensions:

bbox width/depth;
confidence;
CV latency;
detected dimensions.

Low confidence is not ignored: the event log and classification panel show a warning and explain rule-based fallback.

Sensor Simulation

Camera is active in DETECTING.
Laser is active during DETECTING and MOVING_TO_GATE.
Ultrasonic sensor is active at WAITING_AT_GATE and CLASSIFYING.

Each sensor keeps active state, last value, latency and last event timestamp.

Actuator Control

Stop-gate closes at WAITING_AT_GATE.
B route opens the gate and sends item straight.
C route keeps the gate closed and extends pusher C.
D route keeps the gate closed and extends pusher D.
RETURN_HOME retracts mechanisms.

State Machine

Main cycle:

IDLE -> MOVING_TO_CAMERA -> DETECTING -> MOVING_TO_GATE -> WAITING_AT_GATE
-> CLASSIFYING -> ROUTE_TO_B/C/D -> RETURN_HOME -> next item or IDLE

Fault states:

FAULT for jam at gate;
EMERGENCY_STOP for emergency stop scenario.

Both stop conveyor motion and require Reset.

Metrics

The dashboard tracks processed count, success/error count, avg cycle time, throughput, CV latency, actuator latency, queue length, queue delay and conveyor speed.

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