Energy-optimal path planning in the coastal ocean
Name
Subramani_Haley_Lermusiaux_energy_optim_coastal_ocean_JGR2017.pdf
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14.09 MB
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Adobe PDF
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Author(s)
Narayanan Subramani, Deepak
Haley, Patrick
Lermusiaux, Pierre
Date Issued
May 2017
Journal
Journal of Geophysical Research: Oceans
Publisher
American Geophysical Union (AGU)
Citation
Subramani, Deepak N., Patrick J. Haley, and Pierre F. J. Lermusiaux. “Energy-Optimal Path Planning in the Coastal Ocean.” Journal of Geophysical Research: Oceans 122, no. 5 (May 2017): 3981–4003. © 2017 American Geophysical Union
Version
Final published version
Abstract
We integrate data-driven ocean modeling with the stochastic Dynamically Orthogonal (DO) level-set optimization methodology to compute and study energy-optimal paths, speeds, and headings for ocean vehicles in the Middle-Atlantic Bight (MAB) region. We hindcast the energy-optimal paths from among exact time-optimal paths for the period 28 August 2006 to 9 September 2006. To do so, we first obtain a data-assimilative multiscale reanalysis, combining ocean observations with implicit two-way nested multiresolution primitive-equation simulations of the tidal-to-mesoscale dynamics in the region. Second, we solve the reduced-order stochastic DO level-set partial differential equations (PDEs) to compute the joint probability of minimum arrival time, vehicle-speed time series, and total energy utilized. Third, for each arrival time, we select the vehicle-speed time series that minimize the total energy utilization from the marginal probability of vehicle-speed and total energy. The corresponding energy-optimal path and headings are obtained through the exact particle-backtracking equation. Theoretically, the present methodology is PDE-based and provides fundamental energy-optimal predictions without heuristics. Computationally, it is 3–4 orders of magnitude faster than direct Monte Carlo methods. For the missions considered, we analyze the effects of the regional tidal currents, strong wind events, coastal jets, shelfbreak front, and other local circulations on the energy-optimal paths. Results showcase the opportunities for vehicles that intelligently utilize the ocean environment to minimize energy usage, rigorously integrating ocean forecasting with optimal control of autonomous vehicles.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1002/2016JC012231
