ABSTRACT

The underwater domain takes up almost four-fifths of the planet and has significant bearing on human well-being, ecological balance, and accordingly, socio-economic prosperity. However, it poses significant challenges to exploration by autonomous systems, particularly in perception and localization capabilities. Many applications, e.g., in environmental monitoring and ecosystem preservation, would benefit significantly from using autonomous underwater vehicles (AUVs), with improved efficiency, productivity, and safety. In particular, the preservation of the underwater ecosystem is of utmost importance in maintaining proper ecological balance and is a significant undertaking where autonomous underwater systems can be valuable tools. This project will integrate expertise in robotics and marine conservation biology to create novel capabilities for autonomous underwater robotic perception and navigation which will make it possible for robots to identify, track, and localize aquatic species. The research team will integrate theoretical advances in robot vision, learning, and localization on an affordable, open-source autonomous robotic platform using field trials, publications, software, and design and dataset releases, in addition to tutorials and workshops in conferences. The research team will also incorporate research results into course curricula to better prepare students for jobs in robotics and intelligent systems while advancing knowledge in conservation biology. The investigators will pay particular attention to recruit students from underrepresented groups to the research team.

This project will integrate expertise in robotics and marine conservation biology to create novel capabilities for autonomous underwater robotic perception and navigation which will make it possible for robots to identify, track, and localize aquatic species. This research will enable AUVs to act as effective robotic assistants in littoral habitats to manage these ecosystems. Specifically, the investigators will create methods to (1) enhance multimodal underwater imagery specifically for robust detection of aquatic species, (2) use zero- and/or one-shot learning for identifying aquatic species with limited training imagery, and (3) combine acoustic and bathymetric methods for accurate underwater robot localization. The research outcomes will be evaluated both individually and as an integrated, coherent system onboard underwater vehicles at appropriate field locations. Investigators have complementary expertise in underwater robotics and aquatic ecology. The integration of the diverse expertise possessed by the research team will lead to major advances in underwater autonomous robotics and enable the use of AUVs in a domain of significant ecological and economic importance.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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