Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 37 references that are in the map. First posted on arXiv 13 Sep 2022.
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References · 14
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- 2019Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flightcited by 28
- 2021Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transitioncited by 9
- 2021Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehiclescited by 8
- 2022Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehiclescited by 8
- 2020Efficient Exploration in Constrained Environments with Goal-Oriented Reference Pathcited by 5
- 2020Using Soft Actor-Critic for Low-Level UAV Controlcited by 5
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