Intelligent Observing and Multiscale Modeling for Ocean Exploration and Sustainable Utilization
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P.F.J. Lermusiaux, P.J. Haley Jr., C. Mirabito, C. Dahill Massachusetts Institute of Technology
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Project Summary Ongoing MIT-MSEAS Research MSEAS Project-supported Publications Additional Links Background Information
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| This research is sponsored by the MIT Portugal Program. | |||
Project Summary
For intelligent ocean exploration and sustainable ocean utilization, the need for smart autonomous underwater vehicles, surface craft, and small aircrafts is rapidly increasing. Applications include scientific studies, solar-wind-wave energy harvesting, transport and distribution of goods, naval operations, security, acoustic surveillance, communication, search and rescue, marine pollution, ocean cleanup, conservation, fisheries, aquaculture, mining, and monitoring and forecasting. Designing optimal paths leads to cost savings, longer operational time, and environmental protection. Our goal is to develop and apply our optimal planning theory and methodology to increase the efficiency of surface craft and underwater vehicles operating in uncertain dynamic ocean conditions. For the first time, we combine environmental forecasting with stochastic control and risk theory, and employ fundamental partial-differential-equations (PDEs) and efficient level-set solutions for exact reachability and path planning. Our novel proposed ocean applications include energy-optimal path planning, optimal environment harvesting, optimal cleanup, and information-optimal exploration and Bayesian machine learning.
Background information is available below.
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Ongoing MIT-MSEAS Research
Long-Term (Collaborative) MIT-MSEAS Goal:
Our goal is to further develop and apply our exact PDE-based planning theory and data-driven ocean modeling methodology to optimize the efficiency and endurance of ocean vehicles.
Specific Objectives:
- Implement and apply our rigorous theory and schemes for energy-optimal path planning and risk minimization under realistic ocean conditions
- Develop and evaluate mission planning for optimal environmental energy harvesting (e.g. solar, wind, and wave energy; algae biofuels) and optimal dynamic ocean cleanup (e.g. marine plastic and litter; oil spills; natural and man-made sediment plumes)
- Develop information-optimal theory for efficient scientific exploration and Bayesian machine learning of ocean model parameterizations and turbulence closures.
Meetings and Conferences
- MIT Portugal 2020 Annual Conference (Lisbon - October 15, 2021)
- MIT Portugal 2021 Annual Conference (Porto - September 20, 2021)
Publications
MSEAS Project-supported Publications
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Additional Links
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Background Information
Traditionally, path planning has been developed for robots in static environments; however, in the ocean, dynamic currents, waves, and winds significantly affect the motion of vehicles. Traditional algorithms then provide incorrect solutions or are too expensive for real-time use. In the past years, we derived new theory and level-set methods that solve for the time-optimal paths in such dynamic environments exactly and efficiently (Lolla et al., 2014a,b; Lermusiaux et al., 2016, 2017a,b). We applied these advances in real-time with real AUVs, and won all of the races when compared to vehicles following shortest distance paths (Subramani et al., 2017).
We will build upon our very recent results that predict the dynamic collection, cleanup, and sampling paths that optimize the harvesting of a dynamic field using minimum time or energy. Our key idea is a higher dimensional PDE whose characteristic ODEs include the dynamics of collection of the dynamic field. It generalizes the Hamilton–Jacobi (HJ) equation that governs the reachability front for a vehicle in strong dynamic flows. We can thus backtrack much more general optimal paths. For example, our new equations govern paths for collection vessels that optimally harvest the maximum amount of a dynamical field while using minimum resources.
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