Current Status


There is a great need to increase the efficiency of marine research. Building global ocean mapping infrastructure is difficult given the financial costs and human effort required in traditional oceanographic technology. Remote sensing technology (such as multibeam sonar and satellite imagery) is insufficient on its own as it cannot penetrate past the ocean surface. Robotics models that have traditionally been used exclusively on solo missions are relatively limited in scope. Dr. Oscar Pizarro, of the Australian Center for Field Robotics at the University of Sydney is leading a group of engineers, roboticists and oceanographers from Woods Hole Oceanographic Institution, Massachusetts Institute of Technology, University of Rhode Island, and University of Michigan, working to develop a new cost-effective and efficient underwater robotics program. The ultimate goal of the research is the development of autonomous ocean mapping systems capable of scaling up in a cost-effective manner. Expanding from the 2015 Coordinated Robotics that took place in the Timor Sea region, the time the team will come together again off Maui, Hawaii, exploring the the ‘Au’Au Channel and its coral reef system. The 21-day expedition will be dedicated to the goal of characterizing the distribution distribution of reefs by deploying a fleet of autonomous vehicles using automated planning and scheduling tools. The team will test various technologies and gather observations related to several challenges in robotics and machine learning. By developing a group of cooperative, unmanned underwater vehicles, researchers aim to increase the quality and efficiency of data collection and analysis, ocean mapping, and predictive habitat modeling. The different vehicles have varying specialties, including optical imaging, acoustic mapping and water column sampling. Robot-gathered data combined with an interdisciplinary approach stands to advance goals such as detailed mapping of the ocean floor, as well as improve our understanding of the relationships between bathymetry and quantitative habitat information. Learn more here.

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