A3TP: Automated, Accurate, and Adaptive UAV Task Planning for Large-Scale Power Transmission Networks Inspection

Abstract

UAV-assisted inspection is vital for large-scale power transmission networks. Beyond safe trajectory generation, expert-level planning requires jointly optimizing UAV paths and camera configurations, adapting to diverse structures and inspection standards to ensure reliable visual data. We propose A3TP, an automated, accurate, and adaptive UAV task planning system tailored for large-scale power transmission network inspection. A3TP integrates: (1) Expertise-driven task modeling to encode human planning knowledge from historical plans, (2) Geometry-centric planning to generate reference trajectories and shooting parameters for identifying all inspection targets, and (3) Deployment-adaptive trajectory optimization to ensure safety and inspection coverage in complex environments. Operating in a knowledge-driven manner, A3TP achieves up to 55.78% IoU improvement over supervised baselines, over 96% classification accuracy, and trajectory planning results comparable to human experts. In a real-world deployment covering 373 towers across 65.84 km, A3TP reduced planning time by 95.3%, achieved an over 88% expert acceptance rate, and reliably detected field defects. These results demonstrate A3TP's scalability and practical value for maintaining real-world power transmission networks. Code and dataset are available at: https://github.com/fqykinhgt/A3TP.git.