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Sensor Fusion and Anomaly Detection with LLMs
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Linked challenge: Autonomous GEO Satellite RPO & Refueling with Grok-2 and Haystack
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Linked challenge
Autonomous GEO Satellite RPO & Refueling with Grok-2 and Haystack
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Original prompt text with formatting preserved for inspection.
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Implement a simulated sensor suite (e.g., LIDAR, camera, IMU) and develop a sensor fusion algorithm (e.g., an Extended Kalman Filter) to estimate the relative state. Detail how you would integrate Grok-2 (via OpenAI API) with Haystack to process these sensor readings. Specifically, how could Grok-2 analyze patterns in noisy sensor data to identify anomalies or predict potential control issues, and then provide actionable advice for dynamic mission adaptation? This ties into the 'RobustnessToUncertainty' evaluation.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.