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

2022/09/13 by Ricardo Bedin Grando, Junior Costa de Jesus, Grando, Ricardo B. +9
Computer Science · Engineering · #Adaptive Control of Nonlinear Systems #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #Underwater Vehicles and Communication Systems

paper · pdf · doi:10.48550/arxiv.2209.06332

openalex publication_date 2022/09/13 · openalex created_date 2022/09/16 · openalex updated_date 2026/07/28

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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