A Voronoi-Based Potential Field Algorithm for UAV Coverage Path Planning in Arbitrary Polygonal Areas

Coverage Path Planning (CPP) is a critical component for autonomous Unmanned Aerial Vehicle (UAV). However, classic methods struggle with non-convex environments, often yielding suboptimal paths or requiring complex area pre-processing. This paper introduces a novel Voronoi-based Full Coverage Path Planner (VFC-CPP) to generate path-length optimal trajectories for arbitrary polygonal areas. Our approach models the path as a dynamic mass-spring system guided by a potential field. The core innovation is a composite force model that integrates a Voronoi-based centroidal attraction force for uniform coverage, a spacing force to maintain ideal waypoint distance, and a path smoothing force to prevent clustering in sharp corners. VFC-CPP iteratively optimizes waypoint locations in continuous space, naturally adapting to complex boundaries without explicit decomposition. Comprehensive simulations demonstrate that VFC-CPP consistently generates shorter, more efficient paths than boustrophedon decomposition and grid-based TSP methods, especially in non-convex polygons, while achieving full coverage. Our work offers a robust and flexible solution for UAV coverage tasks in realistic, complex-shaped environments.