| Summary: | Trajectory generation and optimization for ocean vehicles has attracted considerable attention from both the research community and the industry. In the industry, the motivation is the reduction of shipping times and fuel costs, and the focus is on large distance routes.In this context, research on ship routing algorithms has shown that the fuel savings attained by such algorithms can be strongly affected by specific patterns present in the ocean currents. In the research community, the focus is on trajectory generation for unmanned or autonomous marine craft with military and/or scientific applications. Here the mission times and distances are typically much shorter, but ocean current magnitudes are greater and vary on smaller time scales. As such there is pressing need for mission planning algorithms which can 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 first-order nonlinear partial differential equation, and data from ocean models is naturally integrated when solving the equation numerically. 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 examples of real-life mission scenarios using data from an ocean model of the Sado river in Portugal. Software-in-the-loop simulations using the LSTS toolchain validate the practical applicability of the approach.
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