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Autonomous marine vehicles play an essential role in many ocean science and engineering applications. Planning time and energy optimal paths for these vehicles to navigate in stochastic dynamic ocean environments are essential to reduce operational costs. In some missions, they must also harvest solar, wind or wave energy (modeled as a stochastic scalar field) and move in optimal paths that minimize net energy consumption. Markov decision processes (MDPs) provide a natural framework for sequential decision making for robotic agents in such environments. However, building a realistic model and solving the modeled MDP becomes computationally expensive in large-scale real-time applications, warranting the need of parallel algorithms and efficient implementation. In this article, we introduce an efficient end-to-end graphical processing unit (GPU)-accelerated algorithm that 1) builds the MDP model (computing transition probabilities and expected one-step rewards) and 2) solves the MDP to compute an optimal policy. We develop methodical and algorithmic solutions to overcome the limited global memory of GPUs by 1) using a dynamic reduced-order representation of the ocean flows; 2) leveraging the sparse nature of the state transition probability matrix; 3) introducing a neighboring subgrid concept; and 4) proving that it is sufficient to use only the stochastic scalar field&#x2019;s mean to compute the expected one-step rewards for missions involving energy harvesting from the environment, thereby saving memory and reducing the computational effort. We demonstrate the algorithm on a simulated stochastic dynamic environment and highlight that it builds the MDP model and computes the optimal policy 600&#x2013;1000 times faster than conventional CPU implementations, making it suitable for real-time use.

Optimal Path Planning of Autonomous Marine Vehicles in Stochastic Dynamic Ocean Flows Using a GPU-Accelerated Algorithm