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Optimal Motion Strategies with Logic-Based Constraints for Ocean Vehicles.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Optimal Motion Strategies with Logic-Based Constraints for Ocean Vehicles./
作者:	de Aguiar, Miguel Campos Pinto Coelho.
面頁冊數:	1 online resource (129 pages)
附註:	Source: Masters Abstracts International, Volume: 84-01.
Contained By:	Masters Abstracts International84-01.
標題:	Autonomous underwater vehicles. -
電子資源:	click for full text (PQDT)
ISBN:	9798835532322

Optimal Motion Strategies with Logic-Based Constraints for Ocean Vehicles.
de Aguiar, Miguel Campos Pinto Coelho.

Optimal Motion Strategies with Logic-Based Constraints for Ocean Vehicles. - 1 online resource (129 pages)

Source: Masters Abstracts International, Volume: 84-01.

Thesis (M.E.)--Universidade do Porto (Portugal), 2019.

Includes bibliographical references

Optimal trajectory generation for ocean vehicles has attracted considerable attention from both the research community and the industry. In the industry, the motivation is the reduction of travel times and fuel costs, and the focus is on long distance routes. Research on ship routing algorithms has shown that the fuel savings attained by such algorithms can be strongly affected by the ocean currents. In the research community, the focus is on trajectory generation for unmanned and autonomous marine craft in military and/or scientific applications. Here the mission times and distances are typically much shorter, but ocean current vary on smaller time scales and their magnitude can be as high as twice the vehicle's maximum speed. As such there is a pressing need for mission planning algorithms that are able to incorporate data from high temporal-spatial resolution ocean models. As mission requirements become more complex, planning methods must also be able to take into account spatial and temporal constraints which arise in scenarios such as multi-stage operations in areas with tidal driven currents.We propose a method for trajectory generation for unmanned marine vehicles based on dynamic programming. The application of dynamic programming techniques converts an optimal control problem to the problem of solving a Hamilton-Jacobi-Bellman equation, which is a nonlinear partial differential equation. Data about ocean flows, produced by High Frequency radar or ocean models, is easily integrated in this framework. Our parallel implementation of a numerical method for solving Hamilton-Jacobi-Bellman equations allows us to obtain the solution in a few minutes for real-life sized problems. Once the equation is solved, optimal trajectories can be calculated from any deployment point in the operational area. Since dynamic programming can be applied to dynamical systems with both discrete and continuous states, the method is extensible to problems involving logic-based constraints. We present an efficient dynamic programming solution for trajectory generation in multi-stage missions. The problem is reduced to solving a sequence of partial differential equations, each of which can be solved by our numerical solver.The method is validated through real-life mission scenarios using data from ocean models of the Tejo and Sado estuaries in Portugal.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798835532322Subjects--Topical Terms:

1468047
Autonomous underwater vehicles.
Index Terms--Genre/Form:

554714
Electronic books.

Optimal Motion Strategies with Logic-Based Constraints for Ocean Vehicles.
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520 $a Optimal trajectory generation for ocean vehicles has attracted considerable attention from both the research community and the industry. In the industry, the motivation is the reduction of travel times and fuel costs, and the focus is on long distance routes. Research on ship routing algorithms has shown that the fuel savings attained by such algorithms can be strongly affected by the ocean currents. In the research community, the focus is on trajectory generation for unmanned and autonomous marine craft in military and/or scientific applications. Here the mission times and distances are typically much shorter, but ocean current vary on smaller time scales and their magnitude can be as high as twice the vehicle's maximum speed. As such there is a pressing need for mission planning algorithms that are able to incorporate data from high temporal-spatial resolution ocean models. As mission requirements become more complex, planning methods must also be able to take into account spatial and temporal constraints which arise in scenarios such as multi-stage operations in areas with tidal driven currents.We propose a method for trajectory generation for unmanned marine vehicles based on dynamic programming. The application of dynamic programming techniques converts an optimal control problem to the problem of solving a Hamilton-Jacobi-Bellman equation, which is a nonlinear partial differential equation. Data about ocean flows, produced by High Frequency radar or ocean models, is easily integrated in this framework. Our parallel implementation of a numerical method for solving Hamilton-Jacobi-Bellman equations allows us to obtain the solution in a few minutes for real-life sized problems. Once the equation is solved, optimal trajectories can be calculated from any deployment point in the operational area. Since dynamic programming can be applied to dynamical systems with both discrete and continuous states, the method is extensible to problems involving logic-based constraints. We present an efficient dynamic programming solution for trajectory generation in multi-stage missions. The problem is reduced to solving a sequence of partial differential equations, each of which can be solved by our numerical solver.The method is validated through real-life mission scenarios using data from ocean models of the Tejo and Sado estuaries in Portugal.
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