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Research Projects

Long-Term Underwater Autonomy for Surveillance and Manipulation

Enabling robot cooperation when communication is hard

Sponsor: Office of Naval Research (ONR)

Senior investigators: Holly Yanco, Reza Ahmadzadeh, Kshitij Jerath, Maru Cabrera, Adam Norton, Paul Robinette

Junior investigators: Kshitij Srivastava, Anveshak Rathore, Brendan Donoghue, Ernie Pellegrino, Ponita Ty, Rachel Major, Sal Sicari

Intelligent underwater robots (both autonomous underwater vehicles, AUVs, and remotely operated vehicles, ROVs) often need to operate in low bandwidth and highly complex environments performing surveillance, inspection, and maintenance tasks. These tasks rely on effective robot perception and manipulation capabilities both with and without a human diver present in the operating area. Accomplishing these tasks requires the development of long-term autonomy technologies. Within this context, autonomous teams comprised of several humans and autonomous vehicles can have a multiplicative effect on the performance of complex missions as compared to those carried out by a single individual. However, resource constraints such low bandwidth and poor visibility can significantly limit team performance and operational success. Our work seeks to resolve these issues in low-bandwidth operating environments by studying: (a) how can we succinctly represent real-time sensory information collected by the team over long timescales (temporal data aggregation) (b) how can we generate concise control actions to the team that are applicable across diverse timescales (supervisory control), and (c) how can we facilitate these two processes by creating effective reduced-order models of the team (macrostate modeling)?
Rescaled models retain traffic behavior.png

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