AeroSOP: Bridging Task Intent and AAV Execution through Executable Procedures

Large language models (LLMs) enable flexible mission planning for autonomous aerial vehicles (AAVs), but reliance on remote reasoning makes mission progress vulnerable to inference delays and communication disruptions. Action failures and changing observations can invalidate planned steps, making reliable continuation depend on execution feedback and updates consistent with the vehicle's current state. We introduce AeroSOP, a framework that decouples base-station reasoning from onboard execution through verifiable, incrementally updatable standard operating procedures (SOPs). The base station composes reusable skills into validated procedures, while an onboard runtime checks step contracts, verifies completion evidence, and maintains explicit execution state without task-level LLM inference. State-compatible patches introduce subsequent tasks at verified task-completion boundaries, preserving completed results and activating atomically after local compatibility and energy checks. Bounded waiting and return-feasibility monitoring regulate continuation under delayed updates and disturbances. Evaluation against eleven baselines across simulated urban missions shows that AeroSOP achieves 79.67% overall success and 97.00% inspection success, with no false completions or invalid proposals observed on inspection and search–confirmation tasks. Its patches achieve 91.0% useful continuation versus 90.0% for state-preserving full refresh, with 95th-percentile switching latency reduced from 6.8 to 4.7 s. Relative to remote skill-by-skill dispatch, AeroSOP reduces control traffic per mission by 75.5% and response latency at 24 AAVs by 79.7%.