Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition
Since the application of Deep Q-Learning to the continuous action domain in Atari-like games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been qualitatively enhanced. Nowadays, modern Deep-RL can be successfully applied to solve a wide range of complex decision-making ta...
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Zusammenfassung: | Since the application of Deep Q-Learning to the continuous action domain in
Atari-like games, Deep Reinforcement Learning (Deep-RL) techniques for motion
control have been qualitatively enhanced. Nowadays, modern Deep-RL can be
successfully applied to solve a wide range of complex decision-making tasks for
many types of vehicles. Based on this context, in this paper, we propose the
use of Deep-RL to perform autonomous mapless navigation for Hybrid Unmanned
Aerial Underwater Vehicles (HUAUVs), robots that can operate in both, air or
water media. We developed two approaches, one deterministic and the other
stochastic. Our system uses the relative localization of the vehicle and simple
sparse range data to train the network. We compared our approaches with a
traditional geometric tracking controller for mapless navigation. Based on
experimental results, we can conclude that Deep-RL-based approaches can be
successfully used to perform mapless navigation and obstacle avoidance for
HUAUVs. Our vehicle accomplished the navigation in two scenarios, being capable
to achieve the desired target through both environments, and even outperforming
the geometric-based tracking controller on the obstacle-avoidance capability. |
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DOI: | 10.48550/arxiv.2103.12883 |