2026-09-19T03:48:07Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1309052021-07-08T18:37:08Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Pierre F.J. Lermusiaux.
Doshi, Manan(Manan Mukesh)
Massachusetts Institute of Technology. Center for Computational Science & Engineering.
Massachusetts Institute of Technology. Center for Computational Science and Engineering
2021-06-04T20:18:12Z
2021-06-04T20:18:12Z
2021
2021
https://hdl.handle.net/1721.1/130905
1251767814
Thesis: S.M., Massachusetts Institute of Technology, Center for Computational Science & Engineering, February, 2021
Cataloged from the official PDF version of thesis.
Includes bibliographical references (pages 55-61).
We develop an exact partial differential equation-based methodology that predicts time-energy optimal paths for autonomous vehicles navigating in dynamic environments. The differential equations solve the multi-objective optimization problem of navigating a vehicle autonomously in a dynamic flow field to any destination with the goal of minimizing travel time and energy use. Based on Hamilton-Jacobi theory for reachability and the level set method, the methodology computes the exact Pareto optimal solutions to the multi-objective path planning problem, numerically solving the equations governing time-energy reachability fronts and optimal paths. Our approach is applicable to path planning in various scenarios, however we primarily present examples of navigating in dynamic marine environments. First, we validate the methodology through a benchmark case of crossing a steady front (a highway flow) for which we compare our results to semi-analytical optimal path solutions. We then consider more complex unsteady environments and solve for time-energy optimal missions in a quasi-geostrophic double-gyre ocean flow field.
by Manan Doshi.
S.M.
S.M. Massachusetts Institute of Technology, Center for Computational Science & Engineering
61 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Computational Science, Engineering.
Energy-time optimal path planning in strong dynamic flows
Thesis
application/pdf
2021-06-04T20:18:12Z
Publication
Master
unknown
unknown
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<Abstract>We develop an exact partial differential equation-based methodology that predicts time-energy optimal paths for autonomous vehicles navigating in dynamic environments. The differential equations solve the multi-objective optimization problem of navigating a vehicle autonomously in a dynamic flow field to any destination with the goal of minimizing travel time and energy use. Based on Hamilton-Jacobi theory for reachability and the level set method, the methodology computes the exact Pareto optimal solutions to the multi-objective path planning problem, numerically solving the equations governing time-energy reachability fronts and optimal paths. Our approach is applicable to path planning in various scenarios, however we primarily present examples of navigating in dynamic marine environments. First, we validate the methodology through a benchmark case of crossing a steady front (a highway flow) for which we compare our results to semi-analytical optimal path solutions. We then consider more complex unsteady environments and solve for time-energy optimal missions in a quasi-geostrophic double-gyre ocean flow field.</Abstract>
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