AI Assisted ArduPilot Log Diagnostics and Configurator Recommendation System

ArduPilot flight logs are currently analyzed manually, making it difficult to understand why crashes or unexpected behavior occur. Users have to inspect multiple signals across time, which requires experience and is not reliable for most people. A Python tool that runs locally and takes a .BIN log as input. The data is aligned across time and processed to extract signals such as attitude error, battery behavior, GPS error and other signals. Physics-based checks detect sustained issues based on flight behavior, while a Hidden Markov Model observes patterns over time to filter out short disturbances. Results are treated as reliable when both agree over a consistent duration. An LLM based agent coordinates the workflow by running the analysis steps and combining their outputs into a clear explanation, while all calculations follow rule-based logic. The analyser adapts to different firmware versions by reading parameters and detecting configuration related issues by comparing them with expected ranges from the ArduPilot codebase. It outputs what happened, when it happened, and why, along with linked telemetry evidence and possible fixes. Deliverables include a complete Copter focused diagnostic pipeline, ML integration, dynamic parameter lookup, structured output generation, and a packaged command line tool with documentation.