Regulation-Aware Static Obstacle Avoidance Navigation Algorithm for Drone-Based Power-Line Inspection

Abstract

Autonomous unmanned-aircraft obstacle avoidance is a mature research area, but comparatively less work addresses controller-level behavior motivated by European ground-risk and no-overflight considerations during an inspection mission that is already in progress. This paper presents PORCE, a deterministic online local-replanning method for waypoint-preserving obstacle avoidance in autonomous power-line inspection missions. The method integrates a waypoint-driven flight controller, a You Only Look Once-based perception front-end, monocular obstacle geopositioning, and a bounded A-star local planner. Detected protected entities are converted into temporary local exclusion regions; nominal navigation is preserved outside a speed-adaptive reaction horizon and temporarily inhibited when a local conflict is detected. In the logged simulation campaign, the audited encounter triggered at 60.1 m, consistent with the 61 m configured horizon at the 8 m/s cruise speed, and the complete evasion runs added less than 1% path length. Three physical flights over the same corridor produced twenty controlled object encounters; the logged responses included both individual detours and a shared bypass for clustered obstacles, followed by route reattachment. The minimum reported surveyed-footprint clearance was 14.21 m against the 12 m exclusion radius. These results support the feasibility and auditability of the bounded local-replanning architecture within the demonstrated scenarios. The method is regulation-aware by design motivation.