English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep Reinforcement Learning (Deep-RL) techniques for motion control have beencontinuously used to deal with decision-making problems for a wide variety ofrobots. Previous works showed that Deep-RL can be applied to perform maplessnavigation, including the medium transition of Hybrid Unmanned AerialUnderwater Vehicles (HUAUVs). These are robots that can operate in both air andwater media, with future potential for rescue tasks in robotics. This paperpresents new approaches based on the state-of-the-art Double CriticActor-Critic algorithms to address the navigation and medium transitionproblems for a HUAUV. We show that double-critic Deep-RL with Recurrent NeuralNetworks using range data and relative localization solely improves thenavigation performance of HUAUVs. Our DoCRL approaches achieved betternavigation and transitioning capability, outperforming previous approaches.

DoCRL: Double Critic Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition