---
name: olfactory-navigation
description: >
  Interprets a time-ordered buffer of Scentience OPU sensor readings to classify plume
  contact state (approaching, turbulent contact, signal loss, near-source), then
  recommends a navigation action with calibrated confidence. Use when controlling a
  robot or UAV through an odor plume, recovering from signal loss, following a chemical
  gradient, or evaluating real-time olfactory source localization.
version: 1.0.0
author: kordelfrance
license: Apache 2.0
compatibility: >
  Works with raw JSON readings from the scentience BLE or Sockets API. No additional
  SDK install required for interpretation. Use the ble-device skill to acquire readings.
  Optional: scentience Navigation Environment (gym-compatible) for simulation and RL.
metadata:
  domain: olfaction
  tags: [navigation, plume-tracking, source-localization, robotics, uav, gradient, casting]
  hardware: [Reconnaisscent, Scentinel]
  depends_on: [ble-device]
  related_papers:
    - "Chasing Ghosts: A Sim2Real Olfactory Navigation Stack (arXiv 2026)"
    - "Emergent Behavior in Evolutionary Swarms for Machine Olfaction (ACM GECCO 2024)"
    - "AI and Olfaction: A Survey on the Sense of Smell for Robotics (ChemRxiv)"
---

## Goal

Given a sliding window of Scentience OPU readings, classify the robot's current plume
interaction state and emit a structured navigation recommendation — including action,
confidence, and reasoning — suitable for direct consumption by a motor controller or
higher-level planner.

## Instructions

### 1. Buffer Readings

Accumulate a sliding window of N readings (N = 10–30 depending on polling rate).
Extract the target compound. Use `VOC` as the default; substitute a specific compound
(e.g., `NH3`, `H2S`) when tracking a known chemical source.

```python
from collections import deque
window = deque(maxlen=30)

async for reading in device.stream_ble(async_=True):
    if reading["STATUS_opuA"] != "active":
        continue
    window.append(reading)
    if len(window) >= 10:
        result = analyze_plume(window, compound="VOC")
        controller.act(result)
```

### 2. Compute Signal Features

```python
import numpy as np

def extract_features(window, compound: str, noise_floor: float = 0.02):
    signal = [r[compound] for r in window]

    # Baseline correction — rolling minimum over the window
    baseline = min(signal)
    normalized = [max(0.0, s - baseline) for s in signal]

    n = len(normalized)
    recent  = normalized[max(0, n - 3):]
    prior   = normalized[max(0, n - 10): max(0, n - 3)]

    short_trend  = np.mean(recent) / (np.mean(prior) + 1e-6)
    long_trend   = float(np.polyfit(range(n), normalized, 1)[0])
    intermittency = sum(1 for s in normalized if s > noise_floor) / n
    peak_snr     = (max(normalized) - np.mean(normalized)) / (np.std(normalized) + 1e-6)

    return {
        "short_trend": short_trend,
        "long_trend": long_trend,
        "intermittency": intermittency,
        "peak_snr": peak_snr,
        "mean_signal": float(np.mean(normalized)),
    }
```

| Feature | Meaning |
|---------|---------|
| `short_trend` | Ratio of recent 3-reading mean to prior 7-reading mean |
| `long_trend` | Linear slope over the full window (positive = strengthening) |
| `intermittency` | Fraction of readings above detection threshold |
| `peak_snr` | Signal quality; values < 2 indicate noise-dominated readings |

### 3. Classify Plume State

Apply this decision matrix (order matters — evaluate top-to-bottom, take the first match):

| State | Trigger conditions | Physical interpretation |
|-------|--------------------|------------------------|
| `NEAR_SOURCE` | `intermittency > 0.7` AND `short_trend < 1.15` | Saturating; likely within source region |
| `APPROACHING` | `short_trend > 1.2` AND `long_trend > 0` | Signal strengthening; moving toward source |
| `CONTACT_TURBULENT` | `intermittency > 0.4` AND `peak_snr > 2` | Inside plume; turbulent filament contact |
| `LOST_PLUME` | `intermittency < 0.15` (after prior contact) | Plume exit or plume shifted |
| `UNKNOWN` | None of the above | Insufficient or noisy signal |

```python
def classify_state(features, had_prior_contact: bool) -> str:
    f = features
    if f["intermittency"] > 0.7 and f["short_trend"] < 1.15:
        return "NEAR_SOURCE"
    if f["short_trend"] > 1.2 and f["long_trend"] > 0:
        return "APPROACHING"
    if f["intermittency"] > 0.4 and f["peak_snr"] > 2:
        return "CONTACT_TURBULENT"
    if f["intermittency"] < 0.15 and had_prior_contact:
        return "LOST_PLUME"
    return "UNKNOWN"
```

Apply **state hysteresis**: require 3 consecutive readings in a new state before
transitioning. Single-frame transitions are treated as noise.

### 4. Map State to Navigation Action

| State | `recommended_action` | Notes |
|-------|---------------------|-------|
| `APPROACHING` | `"advance"` | Maintain heading; increase sampling rate |
| `CONTACT_TURBULENT` | `"hold_and_cast"` | Hold position; lateral sweeps to center on filament |
| `LOST_PLUME` | `"cast_upwind"` | Crosswind casting with upwind bias; expand search radius |
| `NEAR_SOURCE` | `"slow_and_densify"` | Reduce speed; increase spatial sampling density |
| `UNKNOWN` | `"hold"` | Wait for window to fill; do not act on ambiguous state |

If wind vector is available (`ENV_*` fields or external anemometer), bias `cast_upwind`
direction using `atan2(wind_y, wind_x)`. Without wind data, cast orthogonally to the
last known heading.

### 5. Format Output

```json
{
  "timestamp_ms": 1743379215000,
  "compound": "VOC",
  "plume_contact_confidence": 0.82,
  "state": "APPROACHING",
  "trend": "increasing",
  "signal_pattern": "sustained_gradient",
  "source_proximity_hypothesis": "moderate",
  "recommended_action": "advance",
  "confidence": 0.78,
  "reasoning_summary": "Short-term signal ratio 1.34 and positive long-term slope (+0.021 ppm/frame) indicate movement toward source. Intermittency 0.55 consistent with structured plume contact. Confidence 0.78.",
  "window_size": 22,
  "features": {
    "short_trend": 1.34,
    "long_trend": 0.021,
    "intermittency": 0.55,
    "peak_snr": 4.1
  }
}
```

Confidence is derived from `peak_snr` × `intermittency`. Values below 0.5 indicate
ambiguous state — treat `recommended_action` as tentative and pass this to the
controller with reduced authority.

## Examples

### Signal loss — recovery casting
```
Window (VOC, normalized): [0.82, 0.91, 0.78, 0.12, 0.08, 0.05, 0.04, 0.06, 0.03, 0.04]
                                                  ↑ plume exit

Features: intermittency=0.30, peak_snr=1.1, short_trend=0.61
State → LOST_PLUME
recommended_action → "cast_upwind"
reasoning → "Intermittency dropped from 0.80 to 0.30 over 7 frames after prior contact.
             Begin crosswind casting with upwind bias. Expand search radius by 0.5 m
             per cast arm."
```

### Near-source saturation
```
Window (VOC, normalized): [1.2, 1.8, 2.4, 2.9, 2.8, 3.1, 2.9, 3.0, 3.1, 2.8]

Features: intermittency=0.90, short_trend=1.02, peak_snr=1.4
State → NEAR_SOURCE
recommended_action → "slow_and_densify"
reasoning → "High sustained signal (mean 2.62 ppm normalized), intermittency 0.90,
             short-term ratio 1.02 indicates plateau. Likely within 1–3 m of source
             region. Reduce speed to 0.1 m/s; increase sampling to 10 Hz."
```

### Turbulent contact in crosswind
```
Window (VOC, normalized): [0.0, 0.9, 0.0, 1.2, 0.0, 0.8, 0.0, 1.1, 0.0, 0.7]

Features: intermittency=0.50, peak_snr=5.2, short_trend=0.92
State → CONTACT_TURBULENT
recommended_action → "hold_and_cast"
reasoning → "Alternating high/zero pattern (peak_snr 5.2) indicates turbulent filament
             contact. Hold position; execute ±0.3 m lateral casts to locate filament
             center before advancing."
```

## Constraints

- **Signal loss ≠ source absence** — Turbulent plumes produce intermittent contact.
  `LOST_PLUME` triggers recovery casting, not mission abort.
- **Do not act on single spikes** — One reading above threshold is noise until
  `peak_snr > 2` and `intermittency > 0.2` over at least 5 frames.
- **Absolute concentration is unreliable** — Use baseline-corrected normalized values
  only. Raw ppm varies by temperature, humidity, sensor age, and cross-sensitivity.
- **State hysteresis is required** — Three consecutive readings must confirm a new state
  before transitioning. Single-frame state flips indicate noise.
- **Sensor warm-up** — Discard readings from the first 60 s after device power-on.
- **Wind data improves decisions** — Without wind vector, casting is orthogonal to last
  heading. With wind vector, bias upwind using available `ENV_*` or anemometer data.
- **This skill produces recommendations, not commands** — The downstream controller
  decides whether to execute. Always forward `confidence` so the controller can apply
  authority scaling (e.g., ignore actions when confidence < 0.4).
- **Plume Environment (simulation)** — For training and evaluation, use the Scentience
  Plume Environment sandbox (`gym.make("scentience/PlumeEnv-v0")`), which provides
  ground-truth plume state for reward shaping.