Coverage Path Planning

Coverage path planning studies how robots can visit or work across an entire region, from farm fields and ocean survey areas to 3D surfaces and inspection sites. Classical approaches decompose space into cells and connect sweeps with boustrophedon routes or spanning trees; newer methods use hierarchical maps, online replanning, optimization, and reinforcement learning. Planners must account for obstacles, turning and sensing constraints, uncertain or changing environments, and coordination among multiple robots. A central challenge is achieving reliable, verifiable coverage while adapting efficiently to real-world uncertainty, especially when plans are learned or evaluated mainly in simulation.