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Autonomy for Surface Ship Interception and Engagement (AforSSIE)

P.F.J. Lermusiaux,
Dick K.P. Yue, Yuming Liu,
Franz S. Hover,
P.J. Haley, Jr., C. Mirabito,
T. Lolla, S. Booeshaghi

Massachusetts Institute of Technology
Center for Ocean Engineering
Mechanical Engineering
Cambridge, Massachusetts

Joe Edwards, Nicholas B. Pulsone,
Larry Bush, Thomas Sebastian,
Kristen E. Railey

MIT Lincoln Laboratory
Advanced Sensor Techniques Group
Lexington, Massachusetts

Project Summary
Ongoing MIT Research
AforSSIE-supported Publications
Additional AforSSIE Links
Background Information


This research is sponsored by MIT Lincoln Laboratory.

Project Summary

Our long-term goal is to develop autonomy for AUVs to enable intercept and proximity operations with underway surface vessels. In collaboration with the Lincoln Lab researchers and personnel, and other MIT PIs our specific objectives are to:

Background information is available below.

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Ongoing MIT Research

Research Group Roles:

Real-time Sea Exercises (October and December 2016)

Real-time modeling products and data sources can be found on the 2016 Sea Exercises page.

Real-time Sea Exercises (July 2015)

Real-time modeling products and data sources can be found on the 2015 Sea Exercises page.

This page contains links to real-time modeling and processed information (atmospheric forcing, current velocities, ocean synoptic data, and MSEAS-PE ocean forecasts), a listing of data sources used, and some results from our autonomy preparation work.

Presentations and Meetings


With Prof. Panos Tsiotras and his student Wei Sun, we extended our reachability theoretical results to pursuit-evasion games, see this page.

MSEAS AforSSIE-supported Publications

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Additional AforSSIE Links

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Background Information

A specific research motivation is to apply the rigorous theory and schemes recently derived by the MSEAS group for optimal path planning of swarms of autonomous and heterogeneous vehicles operating for long-duration in strong and dynamic currents (Lolla et al, 2012a, b; Lermusiaux et al., 2014; Lolla et al, 2014a, b). The methodology has been extended to energy-optimal paths (Subramani et al., 2014), swarm-optimal paths (Lolla et al., 2014c) that maintain specific inspection or engagement formations, and uncertainty in the flow fields (Lermusiaux et al., 2014). To reduce uncertainties, we plan to provide guidance for optimal sampling (e.g. Lermusiaux, 2007), as needed during the river and sea experiments. A second motivation concerns the water motions around surface vehicles and their effect on the autonomous engagement. The MIT VFRL group has developed advanced computational capabilities (e.g. Zhu, Liu & Yue 2008; Yan & Liu 2011) for accurately predicting the flow characteristics associated with surface ship motions under various operation and environmental conditions. These capabilities enable an accurate prediction of temporal and spatial flow variations associated with the presence of target and background surface ships. A final specific motivation involves network-based feedback control which is an exciting area of current theoretical and algorithmic work in many disciplines. Cooperative underwater vehicle systems highlight many of the key themes because acoustic communication incurs severe rate and interference constraints, as well as stochastic packet loss. We expect two major activities requiring additional research on these topics: the optimization of channel quality through configuration and layout, and pursuit missions by single vehicle and multi-vehicle teams (Reed and Hover, 2014). This overall research will be integrated with the optimality-based autonomy and network-based control, with the ocean flow and wave inputs.

Specific Research Tasks:

Optimal Path Planning and Sampling for Autonomous Ship Engagement


Quantitative Multi-resolution Flow Modeling


Multiscale Ship Wake Modeling and Prediction


Network-based Feedback Control, Communication Optimization and Autonomous Systems

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