Sensory Coverage

Sensory coverage research focuses on optimizing how sensors gather information across a given area, aiming to maximize data acquisition efficiency and completeness. Current efforts involve developing algorithms, often based on optimization frameworks (e.g., MINLP) and graph-based methods, to plan efficient sensor trajectories and handle incomplete or sparse data, including techniques like virtual sensing using deep learning architectures. These advancements are crucial for improving robotic autonomy in tasks like inspection, exploration, and manipulation, as well as enhancing safety in applications such as autonomous driving by mitigating risks associated with limited sensor visibility.