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• W3123902898 abstract "Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights into the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path following and collision avoidance, decision making becomes nontrivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques to develop autonomous agents capable of achieving this hybrid objective without having a priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path following and avoiding collisions towards achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations." @default.
• W3123902898 created "2021-02-01" @default.
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• W3123902898 date "2021-01-25" @default.
• W3123902898 modified "2023-10-17" @default.
• W3123902898 title "Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles" @default.
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• W3123902898 doi "https://doi.org/10.3389/frobt.2020.566037" @default.
• W3123902898 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7874127" @default.
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• W3123902898 hasPublicationYear "2021" @default.
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