Autonomy for Surface Ship Interception and Engagement (AforSSIE)
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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
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Joe Edwards, Nicholas B. Pulsone, Larry Bush, Thomas Sebastian, Kristen E. Railey MIT Lincoln Laboratory
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Project Summary Ongoing MIT Research MSEAS AforSSIE-supported Publications Additional AforSSIE Links Background Information References
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| 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:
- Model and simulate elements of surface ship encounter and develop required AUV autonomy
- Perform flow modeling and autonomy development, and leverage experiments of opportunity and existing assets for in-water demonstrations.
Background information is available below.
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Ongoing MIT Research
Research Group Roles:
- Lermusiaux group: Quantitative Multi-resolution Flow Modeling and Optimal Path Planning and Sampling for Autonomous Ship Engagement
- Yue group: Multiscale Ship Wake Modeling and Prediction
- Hover group: Network-based Feedback Control, Communication Optimization and Autonomous Systems
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
Publications
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
- Advanced Sensor Techniques Group, ISR and Tactical Systems Division, Lincoln Lab
- Project results and data (password protected)
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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
- Utilize and develop our autonomy schemes for optimal ship engagement including simulated reachability studies, time-optimal paths, energy-optimal paths, and uncertainty-optimal paths or other relevant ship engagement criteria
- Use and further develop models specific to the chosen vehicle types and utilize these models
- Further parallelize and optimize the planning and adaptive sampling codes for time-varying realistic ocean flows, using distributed computing as feasible and as needed for the conops
- Apply and quantitatively evaluate our path planning and sampling schemes, accounting for uncertainties of the ocean and wave forecasts
Quantitative Multi-resolution Flow Modeling
- Set-up and apply our MSEAS systems to provide flow fields for evaluating autonomy approaches and for multi-resolution data-driven modeling
- Further develop and utilize our HDG finite-element code for accurate modeling of flows around the AUVs, at varied operating speeds, using inputs from the ocean and wave models
- Complete real-time experiments, providing ocean forecasts, optimal path planning and sampling guidance as needed
Multiscale Ship Wake Modeling and Prediction
- Apply SNOW to obtain phase-resolved nonlinear ocean surface wave-fields for specified wave spectra in the littoral regions including effects of finite water depth and varying currents
- Apply the high-order boundary element method to obtain nonlinear wave-field prediction in the wake of a ship in calm water and understand the characteristic flow and wake patterns associated with ship geometry, forward speed and water depth variation
- Apply the combined nonlinear wave-field simulation capability and ship wake prediction capability to obtain accurate prediction of ship wake flows in the presence of ambient wave-fields and currents. We use the nonlinear wave-field predicted by SNOW as an ambient wave-field and apply the high-order boundary element method to compute nonlinear interactions between ship motion and ambient wave-field and specified currents
Network-based Feedback Control, Communication Optimization and Autonomous Systems
- Develop analysis and algorithms that support such a dual-channel NCS, with particular emphasis on implementation for small networks
- Explore and understand the impact of different sensors' properties on the multi-vehicle pursuit mission
- Refine our existing multi-armed bandit capabilities for general layout and configuration of acoustic networks
- Carry out local tests in the Charles River with our testbed system, and make targeted field outings to larger waters, such as south of Marth's Vineyard (MA)
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References
- Lolla, T.; Ueckermann, M.P.; Yigit, K.; Haley, P.J.; Lermusiaux, P.F.J., 2012, Path planning in time dependent flow fields using level set methods, 2012 IEEE International Conference on Robotics and Automation (ICRA), 166-173, 14-18 May 2012, doi: 10.1109/ICRA.2012.6225364.
- Lolla, T., Lermusiaux, P. F. J., Ueckermann M. P. and P. J. Haley Jr., 2012. Modified level set approaches for the planning of time-optimal paths for swarms of ocean vehicles, MSEAS report-15. Tech. rep., Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA USA.
- Lermusiaux P.F.J, T. Lolla, P.J. Haley. Jr., K. Yigit, M.P. Ueckermann, T. Sondergaard and W.G. Leslie, 2015. Science of Autonomy: Time-Optimal Path Planning and Adaptive Sampling for Swarms of Ocean Vehicles. Chapter 11, Springer Handbook of Ocean Engineering: Autonomous Ocean Vehicles, Subsystems and Control, Tom Curtin (Ed.). In press.
- Lolla, T. and P.F.J. Lermusiaux, 2015. A Forward Reachability Equation for Minimum-Time Path Planning in Strong Dynamic Flows. SIAM Journal on Control and Optimization, sub-judice.
- Lolla, T., P.J. Haley, Jr. and P.F.J. Lermusiaux, 2014. Time-Optimal Path Planning in Dynamic Flows using Level Set Equations: Realistic Applications. Ocean Dynamics, 64, 10:1399–1417. DOI: 10.1007/s10236-014-0760-3.
- Subramani, D.N., T. Lolla, P.J. Haley and P.F.J Lermusiaux, 2015. A Stochastic Optimization Method for Energy-based Path Planning, In: Dynamic Data-driven Environmental Systems Science Conference (Eds. Ravela, Sandu et al), Springer Lecture Notes In Computer Science, In Press. Final Publication will be available at http://link.springer.com/
- Lolla, T., P.J. Haley. Jr. and P.F.J. Lermusiaux, 2015. Path Planning in Multi-scale Ocean Flows: Coordination and Dynamic Obstacles. Ocean Modelling, sub-judice.
- Lermusiaux, P.F.J, 2007. Adaptive Modeling, Adaptive Data Assimilation and Adaptive Sampling. Refereed invited manuscript. Special issue on "Mathematical Issues and Challenges in Data Assimilation for Geophysical Systems: Interdisciplinary Perspectives". C.K.R.T. Jones and K. Ide, Eds. Physica D, Vol 230, 172-196, doi: 10.1016/j.physd.2007.02.014.
- Zhu, Q., Liu, Y. and Yue, D.K.P., 2008. Resonant interaction of Kelvin ship waves and ambient waves. Journal of Fluid Mechanics 597, 171-197.
- Yan, H. and Liu, Y., 2011. An efficient high-order boundary element method for nonlinear wave-wave and wave-body interactions. Journal of Computational Physics 230, 402-424.
- Reed, B. and Hover, F., 2014. Oceanographic pursuit: Networked control of multiple vehicles tracking dynamic ocean features. Methods in Oceanography, doi: 10.1016/j.mio.2014.05.001.
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