A distributed coverage path planning framework for autonomous unmanned aerial vehicle (UAV) swarms

The use of autonomous unmanned aerial vehicle (UAV) swarms for area coverage requires efficient coverage path planning (CPP) strategies that ensure complete exploration while minimizing maneuvering effort, energy consumption, and collision risk. This paper proposes a distributed computational framework for swarm-based patrolling using CPP algorithms. The framework integrates Bézier-curve trajectory smoothing and safety-distance constraints to generate dynamically feasible and collision-free paths. A capability-aware space decomposition method partitions the target region into convex subareas, enabling parallel coverage while accounting for UAV configuration and platform capabilities. Swarm-adapted versions of Parallel, Square, LMAT, and SCAN strategies are developed to generate intra- and inter-subregion coverage paths. Experimental validation using a homogeneous swarm of four quadcopters demonstrates reduced computational complexity and turning maneuvers while producing smooth and continuous trajectories, enabling efficient large-area coverage with improved operational endurance.