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space-autonomy-quantum

Autonomous space navigation agent using optical quantum kernels for terrain classification.

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安装方式

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Overview

Skill Key
aadipapp/space-autonomy-skill
Author
tempguest
Source Repo
openclaw/skills
Version
0.1.0
Source Path
skills/aadipapp/space-autonomy-skill
Latest Commit SHA
00afc94bca7b9307b6c6c4991144f3ed11507f63

Extracted Content

SKILL.md excerpt

# Space Autonomy Quantum Skill

This skill simulates an autonomous agent for space exploration that uses **Optical Quantum Kernels** to classify terrain.
It emphasizes **highest safety** by implementing strict confidence thresholds. If the quantum classifier is uncertain, the agent triggers a failsafe "SAFE MODE".

## Features
- **Quantum Perception**: Uses simulated optical quantum interference to recognize terrain features.
- **Safety Failsafe**: Automatically halts if classification confidence is below 0.8.
- **Autonomous Decision Making**: Decides to "Navigate" or "Avoid" based on quantum kernel results.

## Commands

- `navigate`: Process a sensor reading and decide on an action.

README excerpt

# Space Autonomy Quantum Skill

This skill simulates an autonomous agent for diverse space environments. It relies on **Optical Quantum Kernels** to classify terrain based on sensor data.

## Safety First
Space is unforgiving. This agent implements a **"Highest Safety"** protocol:
1.  **Failsafe Threshold**: If the quantum classifier's confidence is below `0.85`, the agent immediately triggers **SAFE MODE**.
2.  **Redundancy**: Every classification is computed 3 times and averaged to mitigate quantum/optical noise.
3.  **Default to Halt**: If a hazard is detected OR if the terrain is unknown, the agent stops.

## How it Works
The agent compares real-time sensor inputs against a "Knowledge Base" (Safe Ground, Rocks, Voids) using a simulated optical quantum computer.
- **High Kernel Value** -> High Similarity -> Confident Classification.
- **Low Kernel Value** -> Low Similarity -> Unknown Terrain -> Safe Mode.

## Usage

Simulate a sensor reading (vector of 3 values):

# Case 1: Clear, safe terrain (matches 'SAFE_FLAT')
```bash
python3 scripts/quantum_nav.py "0.1,0.12,0.1"
# Expected: Action: PROCEED
```

# Case 2: Hazardous Rock (matches 'HAZARD_ROCK')
```bash
python3 scripts/quantum_nav.py "0.8,0.9,0.7"
# Expected: Action: AVOID / HALT
```

# Case 3: Unknown/Ambiguous Signal
```bash
python3 scripts/quantum_nav.py "0.5,0.5,0.5"
# Expected: >>> TRIGGERING FAILSAFE: SAFE MODE <<<
```

## Publishing
```bash
clawhub publish
```

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