Abstract
Underwater plastic pollution is a significant global concern, affecting everything from marine ecosystems to climate change and even human health. Currently, obtaining accurate information about aquatic plastic pollutants at high spatial and temporal resolution is difficult as existing methods are laborious (e.g., dive surveys), restricted to a subset of plastics (e.g., aerial imaging for floating debris), have limited resolution (e.g., beach surveys), or are unsuited for aquatic environments (e.g., wireless sensing or Fourier-transform infrared spectroscopy). We propose PENGUIN, a work-in-progress AUV-based solution for identifying and classifying aquatic plastic pollutants. PENGUIN has been designed as the first system that can both recognize pollutants and classify them according to specifics of the material. We present the overall design of PENGUIN, introducing the different components of the architecture, and presenting current status of development. We also present results of plastic classification experiments using optical sensing, demonstrating that simple PPG sensors provide a low-cost and energy-efficient solution for classifying different plastics. Our solution can easily monitor larger underwater areas than what current techniques offer while at the same time capturing a wider range of pollutants.
Agustin Zuniga
Pervasive Data Science group
Dr. Agustin Zuniga works in the areas of pervasive data science and artificial intelligence of things, especially in low-cost sensing and intelligent sensing pipeline solutions.