Coverage Optimization

Coverage optimization focuses on efficiently allocating resources, such as sensors or robots, to maximize the coverage of a target area or distribution, often under constraints like limited resources, obstacles, or unknown environments. Current research explores diverse approaches, including heuristic algorithms (e.g., variations of Traveling Salesperson Problem solutions and A* search), variational inference methods (like Stein Variational Gradient Descent), and machine learning techniques (e.g., generative adversarial networks and semi-supervised learning for anomaly detection). These advancements have significant implications for various fields, improving efficiency in tasks ranging from agricultural spraying and environmental monitoring to network deployment and 3D scene reconstruction.