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arXiv · 2209.06332

Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization

Ricardo B. Grando·Junior C. de Jesus·Victor A. Kich·Alisson H. Kolling·Rodrigo S. Guerra·Paulo L. J. Drews-Jr
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Abstract

Previous works showed that Deep-RL can be applied to perform mapless navigation, including the medium transition of Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs). This paper presents new approaches based on the state-of-the-art actor-critic algorithms to address the navigation and medium transition problems for a HUAUV. We show that a double critic Deep-RL with Recurrent Neural Networks improves the navigation performance of HUAUVs using solely range data and relative localization. Our Deep-RL approaches achieved better navigation and transitioning capabilities with a solid generalization of learning through distinct simulated scenarios, outperforming previous approaches.

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Ricardo B. Grando, Junior C. de Jesus, Victor A. Kich, Alisson H. Kolling, Rodrigo S. Guerra, Paulo L. J. Drews-Jr. 2022-09-13. Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization. https://arxiv.org/abs/2209.06332

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