Angelo Odetti

Angelo Odetti, Ph.D., Marine Engineer and Naval Architect, is a Research Scientist at CNR-INM. From 2011 he worked in FP7 project “HoverSpill'' as responsible for technical project and design of a second generation Hovercraft. He patented new concepts for Hovercraft technology. In 2013 he joined CNR as an associate researcher. Its research line is based on the ideation and development of new concept vehicles for access into remote and dangerous areas. He is the designer of various hybrid (ROV-AUV) vehicles: e-URoPe, P2ROV, PROTEUS and Blucy, of the ASV SWAMP expressly designed for extremely shallow waters and of tools, samplers and robotic manipulators. With PROTEUS and SWAMP he took part in three robotic-based scientific in the Arctic environment and one in Antarctica. He is author of more than 50 publications in International Journals/Conferences.

The Anthropocene is characterised by extreme weather events and rapid climatic changes. It is, therefore, fundamental, to understand and forecast the driving processes of the oceanic environment. The importance of monitoring the most remote areas of the planet lays in the uncertainties associated with weather predictions, Earth system understanding, and climate mitigation, to name a few. Smart, modular and reconfigurable unmanned marine vehicles can reach and navigate harsh areas, such as the tidal glaciers sites, making available data that have been so far unreachable. In 2022 INM carried out scientific campaigns both the Arctic and the Antarctic regions, deploying two highly configurable marine robotic platforms: SWAMP (Shallow Water Autonomous Multipurpose Platform ) and PROTEUS (Portable RObotic TEchnology for Underwater Surveys). During the campaigns were gathered novel biogeochemical and physical data and samples, as well as a collection of under ice images and videos. The valorisation of such a unique set of data led to the understanding of the importance of standards and data protocols to make the missions repeatable and to offer to the community a meaningful dataset.