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flux-perception

Multi-sensor fusion engine with confidence-weighted aggregation for FLUX agents.

What This Is

flux-perception is a Rust crate implementing a sensor fusion engine — it takes readings from multiple sensors, each with their own weight, bias, and confidence, and produces a single FusedSignal representing the best estimate of reality.

Role in the FLUX Ecosystem

Agents don't perceive the world through a single lens. flux-perception models how FLUX agents fuse noisy, contradictory inputs into coherent decisions:

  • flux-navigate fuses position estimates from multiple sources
  • flux-compass combines heading readings with sensor data
  • flux-evolve uses fused signals as fitness inputs for behavioral evolution
  • flux-trust cross-references perception confidence with trust scores

Key Features

Feature Description
Weighted Fusion Combines sensor values using confidence × weight averaging
Bias Calibration Per-sensor bias correction via calibrate()
Confidence Threshold Ignores sensors below a minimum confidence floor
Agreement Metric agreement() returns 0.0–1.0 indicating how aligned sensors are
Variance Tracking Reports spread/confidence of the fused signal
History Tracks recent fused signals for trend analysis

Quick Start

use flux_perception::Engine;

let mut engine = Engine::new(0.1); // confidence threshold

// Add sensors with different weights
engine.add_sensor(1, 1.0, 0.0);  // high-weight primary
engine.add_sensor(2, 0.5, 0.0);  // lower-weight secondary

// Feed readings
engine.update(1, 42.0, 0.95, 100);
engine.update(2, 44.0, 0.70, 100);

// Read fused signal
let fused = engine.read();
println!("Value: {:.1}, Confidence: {:.2}", fused.value, fused.confidence);
println!("Agreement: {:.2}", engine.agreement());

// Calibrate a sensor
engine.calibrate(2, -2.0); // correct systematic bias
engine.deactivate(1);       // ignore a faulty sensor

Building & Testing

cargo build
cargo test

Related Fleet Repos

License

Part of the SuperInstance FLUX fleet.

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Rust sensor fusion: confidence-weighted blending, bias, variance, agreement

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