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Armando A. Neto

2 accepted papers

2021

Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition

ICRA 2021poster

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…

Cited by 46SourceScholar
2018

A Comparative Study on Sigma-Point Kalman Filters for Trajectory Estimation of Hybrid Aerial-Aquatic Vehicles

IROS 2018poster

In this paper, a study on nonlinear state estimation methods for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs) is presented. Based on a detailed dynamic model simulation, we analyse and elect the best nonlinear algorithm among those presented in the state-of-the-art literature addressing local…

Cited by 26SourceScholar