Coverage Path

Coverage path planning (CPP) focuses on efficiently generating trajectories for robots or agents to completely cover a given area, minimizing time, energy, or other relevant costs. Current research emphasizes developing algorithms that handle complex, non-convex environments, incorporating constraints like turn costs, energy limitations, and varying coverage demands, often utilizing techniques such as graph-based planning, hierarchical approaches, and deep reinforcement learning. These advancements are crucial for diverse applications, including autonomous exploration, agricultural robotics, and manufacturing processes, improving efficiency and enabling tasks previously impractical or impossible.